KINDAI UNIVERSITY


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SHIRAHAMA Kimiaki

Profile

FacultyDepartment of Informatics
PositionAssociate Professor
DegreeDoctor of Engineering (D.E.)
Commentator Guidehttps://www.kindai.ac.jp/meikan/2150-shirahama-kimiaki.html
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Mail
Last Updated :2020/09/30

Education and Career

Education

  •   1998 04  - 2003 03 , Kobe University, Faculty of Engineering
  •   2003 04  - 2005 03 , Kobe University, Graduate School of Science and Technology
  •   2005 04  - 2007 09 , Kobe University, Graduate School of Science and Technology
  •   2009 10  - 2011 03 , Kobe University, Graduate School of Engineering

Academic & Professional Experience

  •   2018 04 ,  - 現在, Associate Professor, Department of Informatics, Kindai University
  •   2015 06 ,  - 2018 03 , Experienced Postdoc Researcher, Pattern Recognition Group, University of Siegen
  •   2013 06 ,  - 2015 05 , JSPS Postdoctoral Fellowships for Research Abroad, Japan Society for the Promotion of Science (JSPS)
  •   2012 04 ,  - 2014 03 , Assistant Professor, College of Information and Systems, Muroran Institute of Technology
  •   2007 10 ,  - 2012 03 , Assistant Professor, Graduate School of Economics, Kobe University

Research Activities

Research Areas

  • Informatics, Database science, Big Data Analysis
  • Informatics, Perceptual information processing, Sensor Fusion
  • Informatics, Perceptual information processing, Visual Media Processing
  • Informatics, Database science, Information Retrieval
  • Informatics, Database science, Multimedia Information Processing

Research Interests

  • Sensor-based Human Activity Recognition, Data Mining, Machine Learning, Multimedia Information Processing

Published Papers

  • Person recognition using Wi-Fi channel state information in an indoor environment, Taishin Mabuchi, Yoshiaki Taniguchi, Kimiaki Shirahama, Proceedings of IEEE ICCE-TW 2020, Proceedings of IEEE ICCE-TW 2020, Sep. 2020 , Refereed
  • A Polar Model for Fast Object Tracking in 360-degree Camera Images, Ahmad Delforouzi, Seyed Amir, Hossein Tabatabaei, Kimiaki Shirahama, Marcin Grzegorzek, Multimedia Tools and Applications, Multimedia Tools and Applications, 78(7), 9275 - 9297, Apr. 2019 , Refereed
  • Labeling of Partially Occluded Regions via the Multi-Layer CRF, Sergey Kosov, Kimiaki Shirahama, Marcin Grzegorzek, Multimedia Tools and Applications, Multimedia Tools and Applications, 78(2), 2551 - 2569, Jan. 2019 , Refereed
  • Example-based 3D Trajectory Extraction of Objects from 2D Videos, Zeyd Boukhers, Kimiaki Shirahama, Marcin Grzegorzek, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2246 - 2260, Sep. 2018 , Refereed
  • Less Restrictive Camera Odometry Estimation from Monocular Camera, Zeyd Boukhers, Kimiaki Shirahama, Marcin Grzegorzek, Multimedia Tools and Applications, Multimedia Tools and Applications, 77(13), 16199 - 16222, Jul. 2018 , Refereed
  • Environmental Microorganism Classification Using Conditional Random Fields and Deep Convolutional Neural Networks, Sergey Kosov, Kimiaki Shirahama, Chen Li, Marcin Grzegorzek, Pattern Recognition, Pattern Recognition, 77, 248 - 261, May 2018 , Refereed
  • A General Framework for Sensor-based Human Activity Recognition, Lukas Köping, Kimiaki Shirahama, Marcin Grzegorzek, Computers in Biology and Medicine, Computers in Biology and Medicine, 95(1), 248 - 260, Apr. 2018 , Refereed
  • Codebook-based Electrooculography Data Analysis towards Cognitive Activity Recognition, Przemysław Łagodziński, Kimiaki Shirahama, Marcin Grzegorzek, Computers in Biology and Medicine, Computers in Biology and Medicine, 95(1), 277 - 287, Apr. 2018 , Refereed
  • Comparison of Feature Learning Methods for Human Activity Recognition using Wearable Sensors, Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, Marcin Grzegorzek, Sensors, Sensors, 18(2), Article No. 679, Feb. 2018 , Refereed
  • Content-based Image Retrieval of Environmental Microorganisms Using Double-stage Optimisation-based Fusion, Yanling Zou, Chen Li, Kimiaki Shirahama, Tao Jiang, Marcin Grzegorzek, Information Engineering Express (IEE), Information Engineering Express (IEE), 3(4), 43 - 53, Dec. 2017 , Refereed
  • Classification of Physiological Data for Emotion Recognition, Philip Gouverneur, Joanna Jaworek-Korjakowska, Lukas Köping, Kimiaki Shirahama, Pawel Kleczek, Marcin Grzegorzek, Proceedings of The 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017), Proceedings of The 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017), 619 - 627, Jun. 2017 , Refereed
  • Automatic Detection of Blue-whitish Veil as the Primary Dermoscopic Feature, Joanna Jaworek-Korjakowska, Pawel Kleczek, Kimiaki Shirahama, Marcin Grzegorzek, Proceedings of The 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017), Proceedings of The 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017), 649 - 657, Jun. 2017 , Refereed
  • Codebook Approach for Sensor-based Human Activity Recognition, Kimiaki Shirahama, Lukas Köping, Marcin Grzegorzek, Proceedings of The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016 extended abstract), Proceedings of The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016 extended abstract), 197 - 200, Sep. 2016 , Refereed
  • Semantic Indexing based on Focus of Attention Extended by Weakly Supervised Learning, Kimiaki Shirahama, Tadashi Matsumura, Marcin Grzegorzek, Kuniaki Uehara, International Journal on Advances in Software, International Journal on Advances in Software, 8(3-4), 410 - 419, Dec. 2015 , Refereed
  • Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning, Chen Li, Kimiaki Shirahama, Marcin Grzegorzek, Proceedings of The Second International Workshop on Environmental Multimedia Retrieval (EMR 2015), Proceedings of The Second International Workshop on Environmental Multimedia Retrieval (EMR 2015), 9 - 14, Jun. 2015 , Refereed
  • Empowering Semantic Indexing with Focus of Attention, Kimiaki Shirahama, Tadashi Matsumura, Marcin Grzegorzek, Kuniaki Uehara, Proceedings of The Seventh International Conferences on Advances in Multimedia (MMEDIA 2015), Proceedings of The Seventh International Conferences on Advances in Multimedia (MMEDIA 2015), 33 - 36, Apr. 2015 , Refereed
  • Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey, Kimiaki Shirahama, Marcin Grzegorzek, Bipin Indurkhya, Journal of Problem Solving, Journal of Problem Solving, 3(1), 36 - 63, Feb. 2015 , Refereed
  • A Multi-Stage Approach for Automatic Classification of Environmental Microorganisms, Chen Li, Kimiaki Shirahama, Joanna Czajkowska, Marcin Grzegorzek, Fangshu Ma, Beihai Zhou, Proceedings of The 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2013), Proceedings of The 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2013), 364 - 370, Jul. 2013 , Refereed
  • Video Retrieval by Learning Uncertainties in Concept Detection from Imbalanced Annotation Data, Kimiaki Shirahama, Kenji Kumabuchi, Kuniaki Uehara, Proceedings of The Fifth International Conferences on Advances in Multimedia (MMEDIA 2013), Proceedings of The Fifth International Conferences on Advances in Multimedia (MMEDIA 2013), 19 - 24, Apr. 2013 , Refereed
  • Fast and exact processing of large-scale video data based on matrix operation, Kimiaki Shirahama, Kuniaki Uehara, Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, 67(7), J241 - J251, 2013 , Refereed
    Summary:There are two important issues for accurate concept detection in videos. One is to train a concept detector with a large number of training examples. The other is to extract the feature representation of a shot based on descriptors, which are densely sampled in both the spatial and temporal dimensions. This paper describes two fast and exact methods based on matrix operation, where a large amount of data are processed in a batch without any approximation. The first method trains a concept detector based on batch computation of similarities among many training examples. The second method extracts the feature representation of a shot by computing probability densities of many descriptors in a batch. The experimental results validate the efficiency and effectiveness of our methods. In particular, the concept detection result obtained by our methods was ranked top in the annual worldwide competition, TRECVID 2012 Semantic Indexing (light).
  • Video Retrieval by Managing Uncertainty in Concept Detection using Dempster-Shafer Theory, Kimiaki Shirahama, Kenji Kumabuchi, Kuniaki Uehara, Proceedings of The Fourth International Conferences on Advances in Multimedia (MMEDIA 2012), Proceedings of The Fourth International Conferences on Advances in Multimedia (MMEDIA 2012), 71 - 74, Apr. 2012 , Refereed
  • Query by Example by Extracting Inductive Query Definition Using Rough Set Theor, Kimiaki Shirahama, Yuta Matsuoka, Kuniaki Uehara, The Journal of the Institute of Image Information and Television Engineers, The Journal of the Institute of Image Information and Television Engineers, 57(1), J124 - J135, Mar. 2012 , Refereed
  • Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval, Kimiaki Shirahama, Kuniaki Uehara, International Journal of Multimedia Data Engineering and Management (IJMDEM), International Journal of Multimedia Data Engineering and Management (IJMDEM), 2(4), 59 - 75, Dec. 2011 , Refereed
  • Intelligent Video Processing Using Data Mining Techniques, Kimiaki Shirahama, Graduate School of Engineering, Kobe University, Graduate School of Engineering, Kobe University, Jan. 2011 , Refereed
  • Video Retrieval from Few Examples Using Ontology and Rough Set Theory, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of The Second Workshop on Semantic Multimedia Database Technologies (SMDT 2010), Proceedings of The Second Workshop on Semantic Multimedia Database Technologies (SMDT 2010), 5 - 16, Dec. 2010 , Refereed
  • A Novel Topic Extraction Method based on Bursts in Video Streams, Kimiaki Shirahama, Kuniaki Uehara, International Journal of Hybrid Information Technology (IJHIT), International Journal of Hybrid Information Technology (IJHIT), 1(3), 21 - 32, Jul. 2008 , Refereed
  • Video Data Mining: Discovering Topics by Burst Detection in Video Streams, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of ICDM 2007 Workshop on Knowledge Discovery and Data Mining from Multimedia Data and Multimedia Applications (KDM 2007), Proceedings of ICDM 2007 Workshop on Knowledge Discovery and Data Mining from Multimedia Data and Multimedia Applications (KDM 2007), 57 - 62, Oct. 2007 , Refereed
  • Extracting semantic patterns in video using time-constrained sequential pattern mining, Kimiaki Shirahama, Koichi Ideno, Kuniaki Uehara, Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, 60(9), 1473 - 1482, Sep. 2006 , Refereed
    Summary:We developed a technique for video data mining to extract 'semantic patterns' associated with semantically relevant events in videos. First, several types of raw-level metadata are derived from the raw video data in each shot. The metadata is then sequentially aggregated into a multistream. Then, sequential patterns, each of which is a temporally ordered set of raw-level metadata, are extracted from the multistream. The sequential patterns are reduced to likely semantic patterns using two types of time constraints that we introduce: 'semantic event boundaries' and 'temporal localities'. We developed a prototype system and demonstrated the effectiveness of extracted semantic patterns.
  • Extracting Alfred Hitchcock's Know-How by Applying Data Mining Technique, Kimiaki Shirahama, Yuya Matsuo, Kuniaki Uehara, Proceedings of The First International Workshop on Objects Models and Multimedia Technologies (OMMT 2003), Proceedings of The First International Workshop on Objects Models and Multimedia Technologies (OMMT 2003), 43 - 54, Aug. 2003 , Refereed
  • Video Data Mining: Extracting Cinematic Rules from Movie, Yuya Matsuo, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of The Forth International Workshop on Multimedia Data Mining (MDM/KDD 2003), Proceedings of The Forth International Workshop on Multimedia Data Mining (MDM/KDD 2003), 18 - 27, Aug. 2003 , Refereed
  • Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification, Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Xinyu Huang, Marcin Grzegorzek, Sensors, Sensors, 20(15), 4271 - 4271, Jul. 31 2020 , Refereed
    Summary:The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems—Human Activity Recognition (HAR) and Emotion Recognition (ER)—and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.
  • Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition, Muhammad Adeel Nisar, Kimiaki Shirahama, Frédéric Li, Xinyu Huang, Marcin Grzegorzek, Sensors, Sensors, 20(12), 3463 - 3463, Jun. 19 2020 , Refereed
    Summary:This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which are composed of several repetitive and concurrent short movements having temporal dependencies. It is improbable to directly use sensor data to recognize these long-term composite activities because two examples (data sequences) of the same ADL result in largely diverse sensory data. However, they may be similar in terms of more semantic and meaningful short-term atomic actions. Therefore, we propose a two-level hierarchical model for recognition of ADLs. Firstly, atomic activities are detected and their probabilistic scores are generated at the lower level. Secondly, we deal with the temporal transitions of atomic activities using a temporal pooling method, rank pooling. This enables us to encode the ordering of probabilistic scores for atomic activities at the higher level of our model. Rank pooling leads to a 5–13% improvement in results as compared to the other popularly used techniques. We also produce a large dataset of 61 atomic and 7 composite activities for our experiments.
  • On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition, Kimiaki Shirahama, Marcin Grzegorzek, ELECTRONICS, ELECTRONICS, 6(2), Article No. 44, Jun. 2017 , Refereed
    Summary:With the recent spread of mobile devices equipped with different sensors, it is possible to continuously recognise and monitor activities in daily life. This sensor-based human activity recognition is formulated as sequence classification to categorise sequences of sensor values into appropriate activity classes. One crucial problem is how to model features that can precisely represent characteristics of each sequence and lead to accurate recognition. It is laborious and/or difficult to hand-craft such features based on prior knowledge and manual investigation about sensor data. To overcome this, we focus on a feature learning approach that extracts useful features from a large amount of data. In particular, we adopt a simple but effective one, called codebook approach, which groups numerous subsequences collected from sequences into clusters. Each cluster centre is called a codeword and represents a statistically distinctive subsequence. Then, a sequence is encoded as a feature expressing the distribution of codewords. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach.
  • Evaluating contour segment descriptors, Cong Yang, Oliver Tiebe, Kimiaki Shirahama, Ewa Lukasik, Marcin Grzegorzek, MACHINE VISION AND APPLICATIONS, MACHINE VISION AND APPLICATIONS, 28(3-4), 373 - 391, May 2017 , Refereed
    Summary:Contour segment (CS) is the fundamental element of partial boundaries or edges in shapes and images. So far, CS has been widely used in many applications, including object detection/matching and open curve matching. To increase the matching accuracy and efficiency, a variety of CS descriptors have been proposed. A CS descriptor is formed by a chain of boundary or edge points and is able to encode the geometric configuration of a CS. Because many different CS descriptors exist, a structured overview and quantitative evaluation are required in the context of CS matching. This paper assesses 27 CS descriptors in a structured way. Firstly, the analytical invariance properties of CS descriptors are explored with respect to scaling, rotation and transformation. Secondly, their distinctiveness is evaluated experimentally on three datasets. Lastly, their computation complexity is studied. Based on results, we find that both CS lengths and matching algorithms affect the CS matching performance while matching algorithms have higher affection. The results also reveal that, with different combinations of CS descriptors and matching algorithms, several requirements in terms of matching speed and accuracy can be fulfilled. Furthermore, a proper combination of CS descriptors can improve the matching accuracy over the individuals.
  • Shape-based object matching using interesting points and high-order graphs, Cong Yang, Christian Feinen, Oliver Tiebe, Kimiaki Shirahama, Marcin Grzegorzek, PATTERN RECOGNITION LETTERS, PATTERN RECOGNITION LETTERS, 83(3), 251 - 260, Nov. 2016 , Refereed
    Summary:In shape-based object matching, it is important how to fuse similarities between points on a shape contour and the ones on another contour into the overall similarity. However, existing methods face two critical problems. Firstly, since most contour points are involved for possible matchings without taking into account the usefulness of each point, it causes high computational costs for point matching. Secondly, existing methods do not consider geometrical relations characterised by multiple points. In this paper, we propose a shape-based object matching method which is able to overcome these problems. To counteract the first problem mentioned, we devise a shape descriptor using a small number of interesting points which are generated by considering both curvatures and the overall shape trend. We also introduce a simple and highly discriminative point descriptor, namely Point Context, which represents the geometrical and topological location of each interesting point. For the second problem, we employ high-order graph matching which examines similarities for singleton, pairwise and triple relations of points. We validate the robustness and accuracy of our method through a series of experiments on six datasets. (C) 2016 Elsevier B.V. All rights reserved.
  • Object matching with hierarchical skeletons, Cong Yang, Oliver Tiebe, Kimiaki Shirahama, Marcin Grzegorzek, PATTERN RECOGNITION, PATTERN RECOGNITION, 55, 183 - 197, Jul. 2016 , Refereed
    Summary:The skeleton of an object provides an intuitive and effective abstraction which facilitates object matching and recognition. However, without any human interaction, traditional skeleton-based descriptors and matching algorithms are not stable for deformable objects. Specifically, some fine-grained topological and geometrical features would be discarded if the skeleton was incomplete or only represented significant visual parts of an object. Moreover, the performance of skeleton-based matching highly depends on the quality and completeness of skeletons. In this paper, we propose a novel object representation and matching algorithm based on hierarchical skeletons which capture the shape topology and geometry through multiple levels of skeletons. For object representation, we reuse the pruned skeleton branches to represent the coarse- and fine-grained shape topological and geometrical features. Moreover, this can improve the stability of skeleton pruning without human interaction. We also propose an object matching method which considers both global shape properties and fine-grained deformations by defining singleton and pairwise potentials for similarity computation between hierarchical skeletons. Our experiments attest our hierarchical skeleton-based method a significantly better performance than most existing shape-based object matching methods on six datasets, achieving a 99.21% bulls-eye score on the MPEG7 shape dataset. (C) 2016 Elsevier Ltd. All rights reserved.
  • Environmental microbiology aided by content-based image analysis, Chen Li, Kimiaki Shirahama, Marcin Grzegorzek, PATTERN ANALYSIS AND APPLICATIONS, PATTERN ANALYSIS AND APPLICATIONS, 19(2), 531 - 547, May 2016 , Refereed
    Summary:Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. However, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this, we introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular, we develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA.
  • Polar Object Tracking in 360-Degree Camera Images, Ahmad Delforouzi, Seyed Amir Hossein Tabatahaei, Kimiaki Shirahama, Marcin Grzegorzek, PROCEEDINGS OF 2016 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), PROCEEDINGS OF 2016 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 347 - 352, 2016 , Refereed
    Summary:In this paper, a novel method for polar object tracking in output images from wide field of view of the 360-degree cameras is presented. The proposed method is based on the novel polar object selection to track various objects with diverse shapes directly in polar videos with no rectification. A highly overlapping object selection scheme is proposed for the challenging condition in the moving camera scenarios. Innovative color classifiers are proposed to better detection of desired object in the complex backgrounds. Continuous energy minimizing method is used to deal with the in-plane rotation problem. The selection of a polar area of interest is proposed to optimize the implementation cost. Moreover, the known Lukas-Kanade tracking method is exploited in parallel for further improvement of the tracking result. The experimental results demonstrate the success of the proposed method in terms of precision rate tracking the different objects in the polar images.
  • Towards large-scale multimedia retrieval enriched by knowledge about human interpretation, Kimiaki Shirahama, Marcin Grzegorzek, MULTIMEDIA TOOLS AND APPLICATIONS, MULTIMEDIA TOOLS AND APPLICATIONS, 75(1), 297 - 331, Jan. 2016 , Refereed
    Summary:Recent Large-Scale Multimedia Retrieval (LSMR) methods seem to heavily rely on analysing a large amount of data using high-performance machines. This paper aims to warn this research trend. We advocate that the above methods are useful only for recognising certain primitive meanings, knowledge about human interpretation is necessary to derive high-level meanings from primitive ones. We emphasise this by conducting a retrospective survey on machine-based methods which build classifiers based on features, and human-based methods which exploit user annotation and interaction. Our survey reveals that due to prioritising the generality and scalability for large-scale data, knowledge about human interpretation is left out by recent methods, while it was fully used in classical methods. Thus, we defend the importance of human-machine cooperation which incorporates the above knowledge into LSMR. In particular, we define its three future directions (cognition-based, ontology-based and adaptive learning) depending on types of knowledge, and suggest to explore each direction by considering its relation to the others.
  • Unknown Object Tracking in 360-Degree Camera Images, Ahmad Delforouzi, Seyed Amir Hossein Tabatabaei, Kimiaki Shirahama, Marcin Grzegorzek, 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 1798 - 1803, 2016 , Refereed
    Summary:In this paper, a method for unknown object tracking in output images from 360-degree cameras called Modified Training-Learning-Detection (MTLD) is presented. The proposed method is based on the recently introduced Training-Learning-Detection (TLD) scheme in the literature. The flaws of the TLD approach have been detected and significant modifications are proposed to enhance and to elaborate the scheme. Unlike TLD, MTLD is capable of detecting the unknown objects of interest in 360-degree images. According to the experimental results, the proposed method significantly outperforms the TLD method in terms of detection rate and implementation cost.
  • Multiple Human Detection in Depth Images, Muhammad Hassan Khan, Kimiaki Shirahama, Muhammad Shahid Farid, Marcin Grzegorzek, 2016 IEEE 18TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), 2016 IEEE 18TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), 1 - 6, 2016 , Refereed
    Summary:Most human detection algorithms in depth images perform well in detecting and tracking the movements of a single human object. However, their performance is rather poor when the person is occluded by other objects or when there are multiple humans present in the scene. In this paper, we propose a novel human detection technique which analyzes the edges in depth image to detect multiple people. The proposed technique detects a human head through a fast template matching algorithm and verifies it through a 3D model fitting technique. The entire human body is extracted from the image by using a simple segmentation scheme comprising a few morphological operators. Our experimental results on three large human detection datasets and the comparison with the state-of-the-art method showed an excellent performance achieving a detection rate of 94.53% with a small false alarm of 0.82%.
  • Convoy Detection in Crowded Surveillance Videos, Zeyd Boukhers, Yicong Wang, Kimiaki Shirahama, Kuniaki Uehara, Marcin Grzegorzek, Human Behavior Understanding, Human Behavior Understanding, 9997, 137 - 147, 2016 , Refereed
    Summary:This paper proposes detection of convoys in a crowded surveillance video. A convoy is defined as a group of pedestrians who are moving or standing together for a certain period of time. To detect such convoys, we firstly address pedestrian detection in a crowded scene, where small regions of pedestrians and their strong occlusions render usual object detection methods ineffective. Thus, we develop a method that detects pedestrian regions by clustering feature points based on their spatial characteristics. Then, positional transitions of pedestrian regions are analysed by our convoy detection method that consists of the clustering and intersection processes. The former finds groups of pedestrians in one frame by flexibly handling their relative spatial positions, and the latter refines groups into convoys by considering their temporal consistences over multiple frames. The experimental results on a challenging dataset shows the effectiveness of our convoy detection method.
  • STEM CELL MICROSCOPIC IMAGE SEGMENTATION USING SUPERVISED NORMALIZED CUTS, Xinyu Huang, Chen Li, Minmin Shen, Kimiaki Shirahama, Johanna Nyffeler, Marcel Leist, Marcin Grzegorzek, Oliver Deussen, 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 4140 - 4144, 2016 , Refereed
    Summary:A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely annotated images are used first to train and optimise parameters, and then the optimal parameters are inserted into a Normalized Cut segmentation process. Furthermore, we compare our segmentation results using SNCS to another four classical and two state-of-the-art methods. The overall experimental result shows the usefulness and effectiveness of our method over the six comparison methods.
  • ENVIRONMENTAL MICROORGANISM IMAGE RETRIEVAL USING MULTIPLE COLOUR CHANNELS FUSION AND PARTICLE SWARM OPTIMISATION, Yanling Zou, Chen Li, Kimiaki Shirahama, Tao Jiang, Marcin Grzegorzek, 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2475 - 2479, 2016 , Refereed
    Summary:Environmental Microorganisms (EMs) are very diverse and live in every part of the biosphere (rivers, forests, mountains, etc.), playing critical roles in earth's biogeochemical cycles as they are responsible for the decomposition of waste. Currently, a lot of manual efforts through morphological analysis using microscopes have been put on looking for EMs; however, these methods are expensive and time-consuming To enhance the effectiveness of EM information search, we propose a Content-based Image Retrieval approach by using multiple colour channels fusion and Particle Swarm Optimisation (PSO). First, a microorganism image is decomposed into different colour channels for a more discriminative and eventual representation. Then we extract SIFT features and compute the similarity between a query image and EM database images in terms of each colour channel. Finally, PSO is applied to launch weights fusion and obtain the final retrieval result, because it provides a valid space exploration function. Experiments on our EM dataset show the advantage of the proposed multiple colour channels fusion method over each single channel result.
  • Content-Based Microscopic Image Retrieval of Environmental Microorganisms Using Multiple Colour Channels Fusion, Yanling Zou, Chen Li, Kimiaki Shiriham, Florian Schmidt, Tao Jiang, Marcin Grzegorzek, COMPUTER AND INFORMATION SCIENCE, COMPUTER AND INFORMATION SCIENCE, 656, 119 - 130, 2016 , Refereed
    Summary:Environmental Microorganisms (EMs) are usually unicellular and cannot be seen with the naked eye. Though they are very small, they impact the entire biosphere by their omnipresence. Traditional DeoxyriboNucleic Acid (DNA) and manual investigation in EMs search are very expensive and time-consuming, we develop an EM search system based on Content-based Image Retrieval (CBIR) method by using multiple colour channels fusion. The system searches over a database to find EM images that are relevant to the query EM image. Through the CBIR method, the features are automatically extracted from EM images. We compute the similarity between a query image and EM database images in terms of each colour channel. As many colour channels exist, a weight fusion of similarity in different channels is required. We apply Particle Swarm Optimisation (PSO), Fish Swarm Optimisation Algorithm (FSOA), Invasive Weed Optimization (IWO) and Immunity Algorithm (IA) to laugh fusion. Then obtain the re-weighted EM similarity and final retrieval result. Experiments on our EM dataset show the advantage of the proposed multiple colour channels fusion method over each single channel result.
  • Emotion Recognition Based on Physiological Sensor Data Using Codebook Approach, Kimiaki Shirahama, Marcin Grzegorzek, INFORMATION TECHNOLOGIES IN MEDICINE (ITIB 2016), VOL 2, INFORMATION TECHNOLOGIES IN MEDICINE (ITIB 2016), VOL 2, 472, 27 - 39, 2016 , Refereed
    Summary:This paper addresses emotion recognition based on physiological signal sequences (e.g., blood pressure, galvanic skin response and respiration) that can be obtained using state-of-the-art wearable sensors. We formulate this as a machine learning problem to distinguish sequences labelled with a certain emotion from the other sequences. In particular, we explore how to extract a feature that effectively characterises a sequence and yields accurate emotion recognition. With respect to this, existing methods rely on hand-crafted features that are manually defined based on prior knowledge about physiological signals. However, in addition to intensive labour, it is difficult to manually design features which can represent the details of a sequence. To overcome this, we propose a codebook approach where a sequence is represented with a feature describing the distribution of characteristic subsequences, called codewords. These are statistically justified because they are obtained by clustering a large number of subsequences. In addition, the details of the sequence can be maintained by considering the distribution of hundreds of codewords. Experimental results validate the effectiveness of our codebook-based emotion recognition method.
  • Environmental Microbiological Content-Based Image Retrieval System Using Internal Structure Histogram, Yan Ling Zou, Chen Li, Zeyd Boukhers, Kimiaki Shirahama, Tao Jiang, Marcin Grzegorzek, PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015, PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015, 403, 543 - 552, 2016 , Refereed
    Summary:Environmental Microbiology (EM) is an important scientific field, which investigates the ecological usage of different microorganisms. Traditionally, researchers look for the information of microorganisms by checking references or consulting experts. However, these methods are time-consuming and not effective. To increase the effectiveness of EM information search, we propose a novel approach to aid the information searching work using Content-based Image Retrieval (CBIR). First, we use an microorganism image as input data. Second, image segmentation technique is applied to obtain the shape of the microorganism. Third, we extract shape feature from the segmented shape to represent the microorganism. Especially, we use a contour-based shape feature called Internal Structure Histogram (ISH) to describe the shape, which can use angles defined on the shape contour to build up a histogram and represent the structure of the microorganism. Finally, we use Euclidean distances between each ISHs to measure the similarity of different EM images in the retrieval task, and use Average Precision (AP) to evaluate the retrieval result. The experimental result shows the effectiveness and potential of our EM-CBIR system.
  • Interactive tracking of insect posture, Minmin Shen, Chen Li, Wei Huang, Paul Szyszka, Kimiaki Shirahama, Marcin Grzegorzek, Dorit Merhof, Oliver Duessen, PATTERN RECOGNITION, PATTERN RECOGNITION, 48(11), 3560 - 3571, Nov. 2015 , Refereed
    Summary:In this paper, we present an association based tracking approach to track multiple insect body parts in a set of low frame-rate videos. The association is formulated as a MAP problem and solved by the Hungarian algorithm. Different from a traditional track-and-then-rectification scheme, this framework refines the tracking hypotheses in an interactive fashion: it integrates a key frame selection approach to minimize the number of frames for user correction while optimizing the final hypotheses. Given user correction, it takes user inputs to rectify the incorrect hypotheses on the other frames. Thus, the framework improves the tracking accuracy by introducing active key frame selection and interactive components, enabling a flexible strategy to achieve a trade-off between human effort and tracking precision. Given the refined tracks at a bounding box (BB) level, the tip of each body part is estimated, and multiple body parts in a BB are further differentiated. The efficiency and the effectiveness of the framework are verified on challenging video datasets for insect behavioral experiments. (C) 2015 Elsevier Ltd. All rights reserved.
  • Weakly supervised detection of video events using hidden conditional random fields, Kimiaki Shirahama, Marcin Grzegorzek, Kuniaki Uehara, International Journal of Multimedia Information Retrieval, International Journal of Multimedia Information Retrieval, 4(1), 17 - 32, Mar. 01 2015 , Refereed
    Summary:Multimedia Event Detection (MED) is the task to identify videos in which a certain event occurs. This paper addresses two problems in MED: weakly supervised setting and unclear event structure. The first indicates that since associations of shots with the event are laborious and incur annotator’s subjectivity, training videos are loosely annotated as to whether the event is contained or not. It is unknown which shots are relevant or irrelevant to the event. The second problem is the difficulty of assuming the event structure in advance, due to arbitrary camera and editing techniques. To tackle these problems, we propose a method using a Hidden Conditional Random Field (HCRF) which is a probabilistic discriminative classifier with a set of hidden states. We consider that the weakly supervised setting can be handled using hidden states as the intermediate layer to discriminate between relevant and irrelevant shots to the event. In addition, an unclear structure of the event can be exposed by features of each hidden state and its relation to the other states. Based on the above idea, we optimise hidden states and their relation so as to distinguish training videos containing the event from the others. Also, to exploit the full potential of HCRFs, we establish approaches for training video preparation, parameter initialisation and fusion of multiple HCRFs. Experimental results on TRECVID video data validate the effectiveness of our method.
  • Application of content-based image analysis to environmental microorganism classification, Chen Li, Kimiaki Shirahama, Marcin Grzegorzek, BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 35(1), 10 - 21, 2015 , Refereed
    Summary:Environmental microorganisms (EMs) are single-celled or multi-cellular microscopic organisms living in the environments. They are crucial to nutrient recycling in ecosystems as they act as decomposers. Occurrence of certain EMs and their species are very informative indicators to evaluate environmental quality. However, the manual recognition of EMs in microbiological laboratories is very time-consuming and expensive. Therefore, in this article an automatic EM classification system based on content-based image analysis (CBIA) techniques is proposed. Our approach starts with image segmentation that determines the region of interest (EM shape). Then, the EM is described by four different shape descriptors, whereas the Internal Structure Histogram (ISH), a new and original shape feature extraction technique introduced in this paper, has turned out to possess the most discriminative properties in this application domain. Afterwards, for each descriptor a support vector machine (SVM) is constructed to distinguish different classes of EMs. At last, results of SVMs trained for all four feature spaces are fused in order to obtain the final classification result. Experimental results certify the effectiveness and practicability of our automatic EM classification system. (C) 2014 Nalecz Institute of Biocybemetics and Biomedical Engineering. Published by Elsevier Urban & Partner Sp. z o. o. All rights reserved.
  • Extracting 3D Trajectories of Objects from 2D Videos using Particle Filter, Zeyd Boukhers, Kimiaki Shirahama, Frederic Li, Marcin Grzegorzek, ICMR'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, 83 - 90, 2015 , Refereed
    Summary:Depth estimation is a method to estimate the depth information in a 2D image/video, where the original 3D space is projected onto an image plane. This paper introduces a novel extension of depth estimation in the video domain, where we extract 3D trajectories which individually represent the transition of an object in the 3D space. Such 3D trajectories are useful for appropriately characterising spatio-temporal object relations for video event detection. While we extract 3D trajectories by combining depth estimation and object detection results, the major problem is the inconsistency between these results. For example, significantly different depths may be estimated for the region of the same object, and an object region that is appropriately shaped by estimated depths may be missed. To overcome this, we first initialise the 3D position of an object using the frame with the highest consistency between the depth estimation and object detection results. Then, we track the object in the 3D space using particle filter, where a 3D position of the object is modelled as a hidden state to generate its 2D visual appearance. Experimental results demonstrate the effectiveness of our method.
  • Shape-based Object Matching Using Point Context, Cong Yang, Christian Feinen, Oliver Tiebe, Kimiaki Shirahama, Marcin Grzegorzek, ICMR'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, 519 - 522, 2015 , Refereed
    Summary:This paper proposes a novel object matching algorithm based on shape contours. In order to ensure low computational complexity in shape representation, our descriptor is composed by a small number of interest points which are generated by considering both curvatures and the overall shape trend. To effectively describe each point of interest, we introduce a simple and highly discriminative point descriptor, namely Point Context, which represents its geometrical and topological location. For shape matching, we observed that the correspondences are not only dependent on the similarities between these single points in different objects, but they are also related to the geometric relations between multiple points of interest in the same object. Therefore, a high-order graph matching formulation is introduced to merge the single point similarities and the similarities between point triangles. The main contributions of this paper include (i) the introduction of a novel shape descriptor with robust shape points and their descriptors and (ii) the implementation of a high-order graph matching algorithm that solves the shape matching problem. Our method is validated through a series of object retrieval experiments on four datasets demonstrating its robustness and accuracy.
  • Object Detection and Depth Estimation for 3D Trajectory Extraction, Zeyd Boukhers, Kimiaki Shirahama, Frederic Li, Marcin Grzegorzek, 2015 13TH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI), 2015 13TH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI), 1 - 6, 2015 , Refereed
    Summary:To detect an event which is defined by the interaction of objects in a video, it is necessary to capture their spatio-temporal relation. However, the video only displays the original 3D space which is projected onto a 2D image plane. This paper introduces a method which extracts 3D trajectories of objects from 2D videos. Each trajectory represents the transition of an object's positions in the 3D space. We extract such trajectories by combining object detection with depth estimation that estimates the depth information in 2D videos. The major problem for this is the inconsistency between object detection and depth estimation results. For example, significantly different depths may be estimated for the region of the same object, and an object region that is appropriately shaped by estimated depths may be missed. To overcome this, we first initialise the 3D position of an object by selecting the frame with the highest consistency between the object detection and depth estimation results. Then, we track the object in the 3D space using particle filter, where the 3D position of this object is modelled as a hidden state to generate its 2D visual appearance. Experimental results demonstrate the effectiveness of our method.
  • Hybrid negative example selection using visual and conceptual features, Kimiaki Shirahama, Yuta Matsuoka, Kuniaki Uehara, MULTIMEDIA TOOLS AND APPLICATIONS, MULTIMEDIA TOOLS AND APPLICATIONS, 71(3), 967 - 989, Aug. 2014 , Refereed
    Summary:An application of Query-By-Example (QBE) is presented where shots that are visually similar to provided example shots are retrieved. To implement QBE, counter-example shots are required to accurately distinguish shots that are relevant to the query from those that are not (Li and Snoek (2009), Yu et al. (2004)). However, there are usually a huge number of shots, not relevant to a particular query, which can serve as counter-example shots. It is difficult for a user to provide counter-example shots that would aid retrieval. Thus, we developed a QBE method based on partially supervised learning where a retrieval model is constructed by selecting counter-example shots from shots without user supervision. To ensure the speed and accuracy of the QBE method, we select a small number of counter-example shots that are visually similar to given example shots but irrelevant to the query. Such shots are useful for characterizing the boundary between relevant and irrelevant shots. For our method, we first filter shots that are visually dissimilar to example shots based on SVMs on a visual feature. Then we filter shots relevant to the query based on concept detection results from pre-constructed classifiers. Shots that pass the above two tests are considered as counter-example shots. Experimental results obtained using TRECVID 2009 video data validate the effectiveness of our method.
  • Shape-Based Classification of Environmental Microorganisms, Cong Yang, Chen Li, Oliver Tiebe, Kimiaki Shirahama, Marcin Grzegorzek, 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 3374 - 3379, 2014 , Refereed
    Summary:Occurrence of certain environmental microorganisms and their species is a very informative indicator to evaluate environmental quality. Unfortunately, their manual recognition in microbiological laboratories is very time-consuming and expensive. Therefore, we work on an automatic method for shape-based classification of EMs in microscopic images. First, we segment the microorganisms from the background. Second, we describe their shapes by discriminative feature vectors. Third, we perform the EM classification using Support Vector Machines. The most important scientific contribution of this paper, in comparison to the state-of-the-art and to our previous publications in this field, is the introduction of a completely new and very robust 2D feature descriptor for EM shapes. Experimental results certify the effectiveness and practicability of our automatic EM classification system emphasising the benefits achieved with the new shape descriptor proposed in this work.
  • Multimedia event detection using hidden conditional random fields, Kimiaki Shirahama, Marcin Grzegorzek, Kuniaki Uehara, ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014, ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014, 9 - 16, 2014 , Refereed
    Summary:This paper introduces a method for Multimedia Event Detection (MED). Given training videos for a certain event, a classifier is constructed to identify videos displaying it. In particular, the problems of the weakly supervised setting and the unclear event structure are addressed in this paper. The first issue is associated with the loosely annotated training videos that usually contain many irrelevant shots. The second one is the difficulty of assuming the event structure in advance, because videos can be created by arbitrary camera and editing techniques. To overcome these problems, a Hidden Conditional Random Field (HCRF) is used where hidden states work as an intermediate layer to discriminate between relevant and irrelevant shots to the event. In addition, the relation among hidden states characterises the event structure. Thus, the above problems are managed by optimising hidden states and their relation, so as to distinguish videos where the event occurs from the rest of videos. Experimental results on TRECVID video data validate the effectiveness of HCRFs in this context. Copyright 2014 ACM.
  • CLASSIFICATION OF ENVIRONMENTAL MICROORGANISMS IN MICROSCOPIC IMAGES USING SHAPE FEATURES AND SUPPORT VECTOR MACHINES, Chen Li, Kimiaki Shirahama, Marcin Grzegorzek, Fangshu Ma, Beihai Zhou, 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2435 - 2439, 2013 , Refereed
    Summary:Environmental Microorganisms (EMs) are currently recognised using molecular biology (DNA, RNA) or morphological methods. The first ones are very time-consuming and expensive. The second ones require a very experienced laboratory operator. To overcome these problems, we introduce an automatic classification method for EMs in the framework of content-based image analysis in this paper. To describe the shapes of EMs observed in microscopic images, we use Edge Histograms, Fourier Descriptors, extended Geometrical Features, as well as introduce Internal Structure Histograms. For classification, multi-class Support Vector Machine is applied to EMs represented by the above features. In order to quantitatively evaluate discriminative properties of the feature spaces we have introduced, we perform comprehensive experiments with a ground truth of manually segmented microscopic EM images. The best classification result of 89.7% proves a high robustness of our method in this application domain.
  • Event retrieval in video archives using rough set theory and partially supervised learning, Kimiaki Shirahama, Yuta Matsuoka, Kuniaki Uehara, MULTIMEDIA TOOLS AND APPLICATIONS, MULTIMEDIA TOOLS AND APPLICATIONS, 57(1), 145 - 173, Mar. 2012 , Refereed
    Summary:This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.
  • Examining the applicability of virtual reality technique for video retrieval, Kimiaki Shirahama, Kuniaki Uehara, Marcin Grzegorzek, Proceedings - International Workshop on Content-Based Multimedia Indexing, Proceedings - International Workshop on Content-Based Multimedia Indexing, 211 - 216, 2012 , Refereed
    Summary:Query-By-Example (QBE) approach retrieves shots which are visually similar to example shots provided by a user. However, QBE cannot work if example shots are unavailable. To overcome this, this paper develops Query-By-Virtual-Example (QBVE) approach where example shots (virtual examples) for a query are created using virtual reality technique. A virtual example is created by synthesizing the user's gesture, 3D object and background image. Using large-scale video data, we examine the effectiveness of virtual examples from the perspective of video retrieval. In particular, we study about the comparison between virtual examples and example shots selected from real videos, the importance of camera movements, the combination strategy of gestures, 3D objects and backgrounds, and the individual difference in users. © 2012 IEEE.
  • Utilizing Video Ontology for Fast and Accurate Query-by-Example Retrieval, Kimiaki Shirahama, Kuniaki Uehara, FIFTH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2011), FIFTH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2011), 395 - 402, 2011 , Refereed
    Summary:In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach where example shots are provided to represent a query, and used to construct a retrieval model. One drawback of QBE is that a user can only provide a small number of example shots, while each shot is generally represented by a high-dimensional feature. This causes that the retrieval model tends to be overfit to feature dimensions which are specific to example shots, but are ineffective for retrieving relevant shots. As a result, many clearly irrelevant shots are retrieved. To overcome this, we construct a video ontology as knowledge base for QBE. Our video ontology is used to select concepts related to a query. Then, irrelevant shots are filtered by referring to recognition results of objects corresponding to selected concepts. Also, counter-example shots are not provided in QBE, although they are useful for constructing an accurate retrieval model. We introduce a method which selects counter-example shots among shots without user supervision. In this method, our video ontology is used to exclude shots relevant to the query from candidates of counter-example shots. Specifically, we filter shots where object recognition results for concepts related to the query are similar to those of example shots. The effectiveness of our video ontology is tested on TRECVID 2009 video data.
  • Effectiveness of video ontology in query by example approach, Kimiaki Shirahama, Kuniaki Uehara, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6890, 49 - 58, 2011 , Refereed
    Summary:In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach where a query is represented by providing example shots. Relevant shots to the query are then retrieved by constructing a retrieval model from example shots. However, one drawback of QBE is that a user can only provide a small number of example shots, while each shot is generally represented by a high-dimensional feature. In such a case, a retrieval model tends to be overfit to feature dimensions which are specific to example shots, but are ineffective for retrieving relevant shots. As a result, many clearly irrelevant shots are retrieved. To overcome this, we construct a video ontology as a knowledge base for QBE-based video retrieval. Specifically, our video ontology is used to select concepts related to a query. Then, irrelevant shots are filtered by referring to recognition results of objects corresponding to selected concepts. Lastly, QBE-based video retrieval is performed on the remaining shots to obtain a final retrieval result. The effectiveness of our video ontology is tested on TRECVID 2009 video data. © 2011 Springer-Verlag.
  • Query by virtual example: Video retrieval using example shots created by virtual reality techniques, Kimiaki Shirahama, Kuniaki Uehara, Proceedings - 6th International Conference on Image and Graphics, ICIG 2011, Proceedings - 6th International Conference on Image and Graphics, ICIG 2011, 829 - 834, 2011 , Refereed
    Summary:In this paper, we extend the traditional 'Query-By-Example' (QBE) approach where example shots are provided to represent a query, and then shots similar to them are retrieved. One crucial problem of QBE is that when example shots for the query are unavailable, the retrieval cannot be performed. To overcome this, we propose an innovative approach, named 'Query-By-Virtual-Example' (QBVE), where example shots for any arbitrary query can be created by using virtual reality techniques. We call such example shots 'virtual example shots'. In our system, virtual example shots are created by synthesizing user's gesture in front of a video camera, 3D object models (3DCGs) and background images. Experimental results on TRECVTD 2009 video data show the validity of substituting virtual example shots with real example shots used in QBE. © 2011 IEEE.
  • Video Event Retrieval from a Small Number of Examples Using Rough Set Theory, Kimiaki Shirahama, Yuta Matsuoka, Kuniaki Uehara, ADVANCES IN MULTIMEDIA MODELING, PT I, ADVANCES IN MULTIMEDIA MODELING, PT I, 6523, 96 - +, 2011 , Refereed
    Summary:In this paper, we develop an example-based event retrieval method which constructs a model for retrieving events of interest in a video archive, by using examples provided by a user. But, this is challenging because shots of an event are characterized by significantly different features, due to camera techniques, settings and so on. That is, the video archive contains a large variety of shots of the event, while the user can only provide a small number of examples. Considering this, we use "rough set theory" to capture various characteristics of the event. Specifically, by using rough set theory, we can extract classification rules which can correctly identify different subsets of positive examples. Furthermore, in order to extract a larger variety of classification rules, we incorporate "bagging" and "random subspace method" into rough set theory. Here, we define indiscernibility relations among examples based on outputs of classifiers, built on different subsets of examples and different subsets of feature dimensions. Experimental results on TRECVID 2009 video data validate the effectiveness of our example-based event retrieval method.
  • Query by few video examples using rough set theory and partially supervised learning, Kimiaki Shirahama, Yuta Matsuoka, Kuniaki Uehara, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6725, 190 - 191, 2011 , Refereed
    Summary:In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach, where a user represents a query by providing example shots. QBE then retrieves shots similar to example shots in terms of color, edge, motion, etc. We consider QBE as effective because the query is represented by features in example shots without the ambiguity of semantic contents. In addition, QBE can perform retrieval for any queries as long as the user can provide example shots. © 2011 Springer-Verlag.
  • Query by example for large-scale video data by parallelizing rough set theory based on MapReduce, Kimiaki Shirahama, Yanpeng Lin, Yuta Matsuoka, Kuniaki Uehara, CSSR 2010 - 2010 International Conference on Science and Social Research, CSSR 2010 - 2010 International Conference on Science and Social Research, 390 - 395, 2010 , Refereed
    Summary:In this paper, we propose an efficient query-by-example method for large-scale video data. To implement this, we address the following three problems. The first one is that large-scale video data includes many shots relevant to the same query. Since these shots contain significantly different features due to camera techniques and settings, they cannot be retrieved by a single model. Thus, we use "rough set theory" to extract multiple classification rules from example shots. That is, we aim to retrieve a variety of relevant shots where each rule is specialized to retrieve relevant shots containing certain features. The second problem is an expensive computation cost of the retrieval process on large-scale video data. To overcome this, we parallelize the process by using MapReduce, which is a parallel programming model for enabling efficient data distribution and aggregation. The final problem is that large-scale video data includes many shots which contain similar features to example shots, but are clearly irrelevant to the query. Consequently, the retrieval result includes several clearly irrelevant shots. To filter out them, we incorporate a "avideo ontology" as a knowledge base in our method. Experimental results on TRECVID 2009 video data validate the effectiveness of our method. © 2010 IEEE.
  • Example-based event retrieval in video archive using rough set theory and video ontology, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of the 10th International Workshop on Multimedia Data Mining, MDMKDD '10, Proceedings of the 10th International Workshop on Multimedia Data Mining, MDMKDD '10, Article No. 6, 2010 , Refereed
    Summary:In this paper, we develop a method for retrieving events of interest in a video archive. To this end, we address the following two issues. First, due to camera techniques, locations and so on, shots of an event contain significantly different features. So, they cannot be retrieved by a single retrieval model. Thus, we use "rough set theory" to extract multiple classification rules, each of which correctly identifies a subset of shots of the event. Second, although concepts like Person, Car and Cityspace are useful for event retrieval, we need to distinguish between relevant concepts to an event and irrelevant ones. Otherwise, the retrieval performance degrades. So, in order to select concepts relevant to the event, we organize concepts into "video ontology" which is a formal and explicit specification of concepts, concept properties and relations among concepts. Experimental results show both the effectiveness of rough set theory and the one of video ontology. © 2010 ACM.
  • Query-based Video Event Definition Using Rough Set Theory and Video Prototypes, Kimaki Shirahama, Chieri Sugihara, Kuniaki Uehara, IMAGING AND PRINTING IN A WEB 2.0 WORLD; AND MULTIMEDIA CONTENT ACCESS: ALGORITHMS AND SYSTEMS IV, IMAGING AND PRINTING IN A WEB 2.0 WORLD; AND MULTIMEDIA CONTENT ACCESS: ALGORITHMS AND SYSTEMS IV, 7540, 7540B-41, 2010 , Refereed
    Summary:Since a user wants to retrieve a great variety of events, it is impractical to index a video archive with pre-defined events. So, "query-based event definition" is essential to dynamically define events from example videos provided by the user. Especially, we address how to accurately cover a large variation of low-level features in an event. Specifically, due to arbitrary camera techniques and object movements, shots of the same event contain significantly different low-level features. That is, these shots are distributed in different subsets in the space of low-level features. So, we use "rough set theory" to extract each subset where example shots can be correctly classified by a simple combination of low-level features. Based on such subsets, we can retrieve various shots of the same event. But, this retrieval only for a wide coverage is not so accurate, where many irrelevant shots are ranked at top positions. Thus, we re-rank retrieved shots by finely matching them with example shots. With respect to this, since the original representation of a low-level feature is very high-dimensional, we use "video prototypes" which mask irrelevant dimensions to the above matching. Experimental results on TRECVID 2008 video archive show the possibility of our two-step method.
  • Query-Based Video Event Definition Using Rough Set Theory and High-Dimensional Representation, Kimiaki Shirahama, Chieri Sugihara, Kuniaki Uehara, ADVANCES IN MULTIMEDIA MODELING, PROCEEDINGS, ADVANCES IN MULTIMEDIA MODELING, PROCEEDINGS, 5916, 358 - +, 2010 , Refereed
    Summary:In videos, the same event can be taken by different camera techniques and in different situations. So, shots of the event contain significantly different features. In order to collectively retrieve such shots, we introduce a method which defines an event by using "rough set theory". Specifically, we extract subsets where shots of the event can be correctly discriminated from all other shots. And, we define the event as the union of subsets. But, to perform the above rough set theory, we need both positive and negative examples. Note that for any possible event, it is impossible to label a huge number of shots as positive or negative. Thus, we adopt a "partially supervised learning" approach where an event is defined from a small number of positive examples and a large number of unlabeled examples. In particular, from unlabeled examples, we collect negative examples based on their similarities to positive ones. Here, to appropriately calculate similarities, we use "subspace clustering" which finds clusters in different subspaces of the high-dimensional feature space. Experimental results on TRECVID 2008 video collection validate the effectiveness of our method.
  • Mining Event Definitions from Queries for Video Retrieval on the Internet, Kimiaki Shirahama, Chieri Sugihara, Kana Matsumura, Yuta Matsuoka, Kuniaki Uehara, 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009), 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009), 176 - +, 2009 , Refereed
    Summary:Since the amount of videos on the internet is huge and continuously increases, it is impossible to pre- index events in these videos. Thus, we extract the definition of each event from example videos provided as a query. But, different from positive examples, it is impractical to manually provide a variety of negative examples. Hence, we use "partially supervised learning" where the definition of the event is extracted from positive and unlabeled examples. Specifically, negative examples are firstly selected based on similarities between positive and unlabeled examples. Here, to appropriately calculate similarities, we use a "video mask" which represent relevant features based on a typical layout of objects in the event. Then, we extract the event definition from positive and negative examples. In this process, we consider that shots of the event contain significantly different features due to various camera techniques and object movements. In order to cover such a large variation of features, we use "rough set theory" to extract multiple definitions of the event. Experimental results on TRECVID 2008 video collection validate the effectiveness of our method.
  • Query-based video event definition using rough set theory, Kimiaki Shirahama, Chieri Sugihara, Yuta Matsuoka, Kuniaki Uehara, 1st ACM International Workshop on Events in Multimedia - EiMM'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09, 1st ACM International Workshop on Events in Multimedia - EiMM'09, Co-located with the 2009 ACM International Conference on Multimedia, MM'09, 9 - 15, 2009 , Refereed
    Summary:Since events queried by a user range much widely, indexing a video archive with pre-defined events is impractical. Hence, "query-based event definition" is an essential technique where an event is defined from example videos provided by the user. In this paper, we introduce a novel query-based event definition method to cover a large variation of low-level features in the same event. Specifically, due to arbitrary video production techniques, shots of the same event contain significantly different low-level features. Thus, we assume that these shots are distributed in different subsets in a feature space. To extract such subsets, we apply "rough set theory" to example shots relevant to an event (positive examples) and irrelevant example shots (negative examples). Thereby, we can extract different subsets where positive or negative examples can be correctly classified by "decision rules" consisting of specific low-level features. In this process, to avoid extracting over-specialized decision rules, we distinguish important low-level features from unimportant ones using "Multiple Correspondence Analysis (MCA)". Finally, the video archive is searched based on decision rules. Experimental results on TRECVID 2008 video archive show the possibility of our method to achieve the wide coverage of each event. Copyright 2009 ACM.
  • Query by shots: Retrieving meaningful events using multiple queries and rough set theory, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 43 - 52, 2008 , Refereed
    Summary:In videos, even if events have the same semantic content (e.g. conversation, battle, chase and so on), they are presented in different ways. So, these events have significantly different properties. For example, one battle event is characterized by a large sound volume (e.g. gunshot), while another one is characterized by a large amount of motion (e.g. fighting action). Thus, events with the same semantic content cannot be internally defined by a single model. In this paper, we propose an example-based event retrieval method which uses multiple queries to externally define events with the same semantic content. Specifically, we apply rough set theory to multiple queries. Thereby, we can extract subsets of events, which are characterized by different combinations of low-level features. Then, by unifying extracted subsets, we conceptualize the semantic content. The experimental results indicate a possibility of our method based on multiple queries and rough set theory. Copyright 2008 ACM.
  • A novel topic extraction method based on bursts in video streams, Kimiaki Shirahama, Kuniaki Uehara, MUE: 2008 INTERNATIONAL CONFERENCE ON MULTIMEDIA AND UBIQUITOUS ENGINEERING, PROCEEDINGS, MUE: 2008 INTERNATIONAL CONFERENCE ON MULTIMEDIA AND UBIQUITOUS ENGINEERING, PROCEEDINGS, 249 - 252, 2008 , Refereed
    Summary:In this paper we introduce a novel topic extraction method. Firstly, we divide a video into events based on target character's appearance and disappearance. Specifically, each event is an interval where the target character performs a certain action. And, it is characterized by a specific pattern of shots where the character appears and shots where he/she disappears. Then, we define a "topic" as an event where the target character performs an interesting action (e.g. fight, chase, kiss and so on). We extract such topics as events containing abnormal patterns, called "bursts". The experiments on different videos validate that character's appearance and disappearance are effective for obtaining semantically meaningful events. From these events, we could extract many interesting topics.
  • A time-constrained sequential pattern mining for extracting semantic events in videos, Kimiaki Shirahama, Koichi Ideno, Kuniaki Uehara, Multimedia Data Mining and Knowledge Discovery, Multimedia Data Mining and Knowledge Discovery, 404 - 426, 2007 , Refereed
    Summary:In this chapter, we present a time-constrained sequential pattern mining method for extracting semantic patterns associated with semantically relevant events (semantic events) in videos. Since a video itself is just a rawmaterial, we transform the video into a multistream of rawlevel metadata. © 2007 Springer.
  • Content-based video retrieval using video ontology, Kimiaki Shirahama, Kazuyuki Otaka, Kuniaki Uehara, ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS, ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS, 283 - 288, 2007 , Refereed
    Summary:In this paper we aim to efficiently retrieve various kinds of events (e.g. conversation, battle, run/walk and so on) front a video archive. To this end, we construct a "video ontology" which is a formal and explicit specification of events. Specifically, an event is modeled to have 4 dimensions of semantic contents (i. e. Action, Location, Time and Shooting technique). For retrieving such events, concepts in 4 dimensions need to be automatically detected. So, we conduct "video data mining" to extract "semantic patterns "from videos. Here, a semantic pattern is a combination of low-level features (e.g. color motion and audio) associated with events of a certain kind. Thus, semantic patterns can be used to characterize concepts in 4 dimensions of semantic contents. Furthermore, we refine the video ontology by extracting new semantic patterns from subspaces of videos, which cannot be retrieved by previously extracted patterns. Finally, we classify each event into video genres which potentially contain this event. It is useful for limiting video genres from which events of user's interest are retrieved.
  • Video data mining: Mining semantic patterns with temporal constraints from movies, K Shirahama, K Ideno, K Uehara, ISM 2005: SEVENTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA, PROCEEDINGS, ISM 2005: SEVENTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA, PROCEEDINGS, 598 - 604, 2005 , Refereed
    Summary:For efficient video data management, 'video data mining' is required to discover 'semantic patterns' which are not only previously unknown and interesting, but also associated with semantically relevant events ('semantic events') in movies. In order to extract semantic patterns from a movie, we firstly represent it as a multi-stream of raw level metadata that abstracts the semantic information of the movie. Then, regarding to the temporal characteristic of the semantic event of the movie, we extract sequential patterns which are obtained by connecting temporally close and strongly associated symbols in the multi-stream of raw level metadata. We also propose a parallel data mining method in order to reduce the expensive computational cost. Finally, we verify whether the extracted patterns call be considered as semantic patterns or not.
  • Mining semantic structures in movies, K Shirahama, Y Matsuo, K Uehara, APPLICATIONS OF DECLARATIVE PROGRAMMING AND KNOWLEDGE MANAGEMENT, APPLICATIONS OF DECLARATIVE PROGRAMMING AND KNOWLEDGE MANAGEMENT, 3392, 116 - 133, 2005 , Refereed
    Summary:'Video data mining' is a technique to discover useful patterns from videos. It plays an important role in efficient video management. Particularly, we concentrate on extracting useful editing patterns from movies. These editing patterns are useful for an amateur editor to produce a new, more attractive video. But, it is essential to extract editing patterns associated with their semantic contents, called 'semantic structures'. Otherwise the amateur editor can't determine how to use the extracted editing patterns during the process of editing a new video. In this paper, we propose two approaches to extract semantic structures from a movie, based on two different time series models of the movie. In one approach, the movie is represented as a multi-stream of metadata derived from visual and audio features in each shot. In another approach, the movie is represented as one-dimensional time series consisting of durations of target character's appearance and disappearance. To both time series models, we apply data mining techniques. As a result, we extract the semantic structures about shot transitions and about how the target character appears on the screen and disappears from the screen.
  • Video data mining: Rhythms in a movie, K Shirahama, K Iwamoto, K Uehera, 2004 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXP (ICME), VOLS 1-3, 2004 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXP (ICME), VOLS 1-3, 1463 - 1466, 2004 , Refereed
    Summary:The task to discover useful editing patterns from a professional video, such as a movie, is one of the main purpose of video data mining. These patterns successfully convey editor's intentions to the viewers. But, data mining on multimedia data like a movie is a challenging task, due to the complicated contents of multimedia data. Particularly, the discovered patterns need to be supported by their semantic features, because these features tell the amateur editors how to use the corresponding patterns during the process of editing a new video. In this paper, we focus on the rhythm in a movie, consisting of the durations of target character's appearance and disappearance. Based on this rhythm, we divide the movie into topics. Each topic corresponds to one meaningful episode of the character. By investigating such topics, we can discover useful editing patterns of character's rhythm, supported by their semantic features. Also, these rhythms can be used to annotate certain types of topics.

Books etc

  • Multimedia Data Mining and Knowledge Discovery (Valery A. Petrushin and Latifur Khan eds.), Kimiaki Shirahama, Kenji Kumabuchi, Marcin Grzegorzek, Kuniaki Uehara, Joint author, Multimedia Data Mining and Analytics: Disruptive Innovation (Aaron K. Baughman, Jiang Gao, Jia-Yu Pan and Valery Petrushin eds.), Springer International Publishing,   2015 04
  • A Time-constrained Sequential Pattern Mining for Extracting Semantic Events in Videos, Kimiaki Shirahama, Koichi Ideno, Kuniaki Uehara, Joint author, Multimedia Data Mining and Knowledge Discovery (Valery A. Petrushin and Latifur Khan eds.), Springer,   2006 12
  • Mining Semantic Structures in Movies, Kimiaki Shirahama, Yuya Matsuo, Kuniaki Uehara, Contributor, Applications of Declarative Programming and Knowledge Management Lecture Notes in Artificial Intelligence (D. Seipel, M. Hanus, U. Geske and O. Bartenstein eds.), Springer,   2005 04

Conference Activities & Talks

  • Recognition of Activities of Daily Living: How to Extract High-level Activities from Raw Sensor Data, Kimiaki Shirahama, The Sixth International Conference on Information Technology in Biomedicine (ITIB 2018),   2018 06 18
  • Kobe University, NICT and University of Siegen at TRECVID 2017 AVS Tas, Zhenying He, Takashi Shinozaki, Kimiaki Shirahama, Marcin Grzegorzekz, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2017 Workshop,   2017 11 14
  • Codebook Approach for Physiological and Behavioural Data Analysis, Kimiaki Shirahama, Marcin Grzegorzek, The Seventh International Week "Internet Communication Management",   2017 04 26 , 招待有り
  • Kobe University, NICT and University of Siegen at TRECVID 2016 AVS Task, Yasuyuki Matsumoto, Takashi Shinozaki, Kimiaki Shirahama, Marcin Grzegorzek, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2016 Workshop,   2016 11 14
  • Multimedia Sensing: Extracting High-level Semantic Information from Low-level Multimedia Data, Kimiaki Shirahama, The Seventh Kobe University Brussels European Centre Symposium,   2016 11 08 , 招待有り
  • University of Siegen, Kobe University and NICT at TRECVID 2015 SIN and MED Tasks, Kimiaki Shirahama, Takashi Shinozaki, Yasuyuki Matsumoto, Marcin Grzegorzek, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2015 Workshop,   2015 11 17
  • University of Siegen, Kobe University, and Muroran Institute of Technology at TRECVID 2013 Multimedia Event Detection, Kimiaki Shirahama, Chen Li, Marcin Grzegorzek, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2013 Workshop,   2013 11 21
  • Kobe University and Muroran Institute of Technology at TRECVID 2012 Semantic Indexing Task, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2012 Workshop,   2012 11 26
  • Kobe University at TRECVID 2011 Semantic Indexing and Multimedia Event Detection, Kimiaki Shirahama, Lin Yanpeng, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2011 Workshop,   2011 12 06
  • Kobe University at TRECVID 2009 Search Task, Kimiaki Shirahama, Chieri Sugihara, Yuta Matsuoka, Kana Matsumura, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2009 Workshop,   2009 11 16
  • TRECVID 2008 NOTEBOOK PAPER: Interactive Search Using Multiple Queries and Rough Set Theory, Akihito Mizui, Kimiaki Shirahama, Kuniaki Uehara, Proceedings of TREC Video Retrieval Evaluation (TRECVID) 2008 Workshop,   2008 11 17
  • Characteristics of Textual Information in Video Data from the Perspective of Natural Language Processing, Kimiaki Shirahama, Akihito Mizui, Kuniaki Uehara, Proceedings of NSF Sponsored Symposium on Semantic Knowledge Discovery, Organization and Use,   2008 11 15

Misc

  • A study on estimation of person using Wi-Fi channel state information, Taishin Mabuchi, Yoshiaki Taniguchi, Kimiaki Shirahama, Proceedings of the 82nd National Convention of IPSJ, 3, 285, 286,   2020 03
  • Analyzing Economic Data Using Value-Based and Shape-Based Time-Series Similarity Measures, SHIRAHAMA KIMIAKI, 204, 5, 71, 79,   2011 11

Awards & Honors

  •   2019 05 , Computers in Biology and Medicine journal, CBM Honors Paper 2018, A general framework for sensor-based human activity recognition
  •   2016 11 , National Institute of Standards and Technology (NIST), Second place at TRECVID 2016 Ad-hoc Video Search (AVS) task (manually-assisted)
  •   2015 04 , International Academy, Research, and Industry Association (IARIA), Top paper, Empowering Semantic Indexing with Focus of Attention
  •   2014 04 , The Forth ACM International Conference on Multimedia Retrieval (ICMR 2014), Top paper, Multimedia Event Detection Using Hidden Conditional Random Fields
  •   2012 12 , IEICE Data Engineering Work Group, Student Honorable Mention, Online Video Annotation Game with Active Learning and Tag Ranking
  •   2012 11 , National Institute of Standards and Technology (NIST), Top performance at TRECVID 2012 Semantic Indexing (lite) task

Research Grants & Projects

  • Bundesministerium für Bildung und Forschung (BMBF), Adaptive Learning System: For an Intuitive Interaction between Human and Complex Techniques, Cognitive Village: Adaptively Learning Technical Support System for Elderly
  • Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (B), Development of Video Retrieval Engine by Using a Large-scale Video Corpus
  • Japan Society for the Promotion of Science (JSPS), JSPS Postdoctoral Fellowships for Research Abroad, High-level Motion Recognition Using Virtual Examples Created by Virtual Reality Technique
  • Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (B), Video Retrieval System based on Machine Learning
  • Ministry of Internal affairs and Communications, Strategic Information and Communications R&D Promotion Programme (SCOPE), Gesture-based Video Archive Retrieval Using Mixed Reality Technique
  • Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Young Scientists (B), Second-generation Video Mining: Extracting Patterns for Exhaustive Retrieval of Events in Video Archive