論文 - 椋木 雅之
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Locality based discriminative measure for multiple-shot human re-identification 査読あり
Wei Li, Yang Wu, Masayuki Mukunoki, Yinghui Kuang, Michihiko Minoh
Neurocomputing 167 280 - 289 2015年11月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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Clustering scenes in cooking video guided by object access 査読あり
Yuki Matsumura, Atsushi Hashimoto, Shinsuke Mori, Masayuki Mukunoki, Michihiko Minoh
Work Shop on Multimedia Cooking and Eating Activities (CEA2015) 2015年6月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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修学旅行事例から見た教育旅行向けモバイル情報サービスの事業モデルと市場戦略-MICE市場での可能性について- 査読あり
笠原秀一，森幹彦, 椋木雅之，美濃導彦
観光情報学会学会誌「観光と情報」 11 ( 1 ) 87 - 98 2015年6月
記述言語：日本語 掲載種別：研究論文（学術雑誌）
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Transportation Mode Annotation of Tourist GPS Trajectories Under Environmental Constraints 査読あり
Hidekazu Kasahara, Mikihiko Mori, Masayuki Mukunoki, Michihiko Minoh
Information and Communication Technologies in Tourism 2015, Springer 523 - 535 2015年2月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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防犯カメラ映像における条件分割型適合性フィードバックによる特定人物画像検索 査読あり
井関洋平, 川西康友, 椋木雅之, 美濃導彦
信学論 J98-D ( 1 ) 236 - 249 2015年1月
記述言語：日本語 掲載種別：研究論文（学術雑誌）
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Person re-identification by common-near-neighbor analysis 査読あり
Li W., Mukunoki M., Kuang Y., Wu Y., Minoh M.
IEICE Transactions on Information and Systems E97D ( 11 ) 2935 - 2946 2014年11月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：IEICE Transactions on Information and Systems
© 2014 The Institute of Electronics, Information and Communication Engineers. Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations and small inter-class differences, continues to prevent these measures from achieving satisfactory re-identification performance. Recognizing neighboring distribution can provide additional useful information to help tackle the deviation of the to-be-measured samples, we propose a novel dissimilarity measure from the neighborhood-wise relative information perspective, which can deliver the effectiveness of those well-distributed samples to the badly-distributed samples to make intra-class dissimilarities smaller than inter-class dissimilarities, in a learned discriminative space. The effectiveness of this method is demonstrated by explanation and experimentation.
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Discriminativ collaborative representation for classification 査読あり
Wu Y., Li W., Mukunoki M., Minoh M., Lao S.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9006 205 - 221 2014年11月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
© Springer International Publishing Switzerland 2015. The recently proposed l2-norm based collaborative representation for classification (CRC) model has shown inspiring performance on face recognition after the success of its predecessor — the l1-norm based sparse representation for classification (SRC) model. Though CRC is much faster than SRC as it has a closed-form solution, it may have the same weakness as SRC, i.e., relying on a “good” (properly controlled) training dataset for serving as its dictionary. Such a weakness limits the usage of CRC in real applications because the quality requirement is not easy to verify in practice. Inspired by the encouraging progress on dictionary learning for sparse representation, which can much alleviate this problem, we propose the discriminative collaborative representation (DCR) model. It has a novel classification model well fitting its discriminative learning model. As a result, DCR has the same advantage of being efficient as CRC, while at the same time showing even stronger discriminative power than existing dictionary learning methods. Extensive experiments on nine widely used benchmark datasets for both controlled and uncontrolled classification tasks demonstrate its consistent effectiveness and efficiency.
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Kokura T., Kawanishi Y., Mukunoki M., Minoh M.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9010 587 - 601 2014年11月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
© Springer International Publishing Switzerland 2015. We tackle multiple people tracking across multiple nonoverlapping surveillance cameras installed in a wide area. Existing methods attempt to track people across cameras by utilizing appearance features and spatio-temporal cues to re-identify people across adjacent cameras. @ However, in relatively wide public areas like a shopping mall, since many people may walk and stay arbitrarily, the spatio-temporal constraint is too strict to reject correct matchings, which results in matching errors. Additionally, appearance features can be severely influenced by illumination conditions and camera viewpoints against people, making it difficult to match tracklets by appearance features. These two issues cause fragmentation of tracking trajectories across cameras. We deal with the former issue by selectively relaxing the spatio-temporal constraint and the latter one by introducing a route cue. We show results on data captured by cameras in a shopping mall, and demonstrate that the accuracy of across-camera tracking can be significantly increased under considered settings.
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Improving hough based pedestrian detection accuracy by using segmentation and pose subspaces 査読あり
Vansteenberge J., Mukunoki M., Minoh M.
IEICE Transactions on Information and Systems E97D ( 10 ) 2760 - 2768 2014年10月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：IEICE Transactions on Information and Systems
Copyright © 2014 The Institute of Electronics, Information and Communication Engineers. The Hough voting framework is a popular approach to parts based pedestrian detection. It works by allowing image features to vote for the positions and scales of pedestrians within a test image. Each vote is cast independently from other votes, which allows for strong occlusion robustness. However this approach can produce false pedestrian detections by accumulating votes inconsistent with each other, especially in cluttered scenes such as typical street scenes. This work aims to reduce the sensibility to clutter in the Hough voting framework. Our idea is to use object segmentation and object pose parameters to enforce votes' consistency both at training and testing time. Specifically, we use segmentation and pose parameters to guide the learning of a pedestrian model able to cast mutually consistent votes. At test time, each candidate detection's support votes are looked upon from a segmentation and pose viewpoints to measure their level of agreement. We show that this measure provides an efficient way to discriminate between true and false detections. We tested our method on four challenging pedestrian datasets. Our method shows clear improvements over the original Hough based detectors and performs on par with recent enhanced Hough based detectors. In addition, our method can perform segmentation and pose estimation as byproducts of the detection process.
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Kasahara H., Kurumatani K., Mori M., Mukunoki M., Minoh M.
Information Technology and Tourism 14 ( 3 ) 197 - 217 2014年9月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Information Technology and Tourism
© 2014, Springer-Verlag Berlin Heidelberg. The recent earthquake in Japan showed that tourists cannot access evacuation information and the families of tourists experienced problems when accessing safety information related to tourists. Given these problems, we consider two issues related to information provision in disaster situations. The first issue is the lack of evacuation information for tourists. The second issue is the difficulty of confirming the safety of tourists and sharing their safety information with relevant people, including the tourist’s family. The present study focuses on developing a tourism information system to solve these issues. We refer to this system as an Educational Trip Support System (ETSS). The research subject is a school trip, which is a representative type of group tour that occurs in Japan. The objectives of the ETSS are to help students to escape to an evacuation area rapidly by providing evacuation information and to share safety confirmations with relevant people during disaster situations. We assessed the effectiveness based on a field test in a disaster-simulated situation and quantitative surveys. The major contributions of this study include (1) a description of a mobile application system for confirming safety during school trips and sharing information with relevant people, (2) a method to facilitate the rapid evacuation of students that saves time and reduces their concerns about the situation, (3) detailed evaluations of the performance obtained using ETSS.
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Can feature-based inductive transfer learning help person re-identification? 査読あり
Wu Y., Li W., Minoh M., Mukunoki M.
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings 2812 - 2816 2013年12月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Person re-identification concerns about the problem of recognizing people across space (captured by different cameras) and/or over time gaps. Though recently the literature on it grows rapidly, all the proposed solutions have treated it as a normal classification or ranking problem. In this paper, however, we argue that it is in fact a natural transfer learning problem, thus it's valuable and also necessary to investigate how the progress on transfer learning could benefit the research on it. We present so far the first study on justifying the effectiveness of a representative transfer learning methodology: feature-based inductive transfer learning, for person re-identification. Extensive experiments on standard datasets with typical methods result in several important findings. © 2013 IEEE.
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Collaboratively Regularized Nearest Points for Set Based Recognition 査読あり
Yang Wu, Michihiko Minoh, Masayuki Mukunoki
4th British Machine Vision Conference (BMVC2013) 2013年9月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Riemannian Set-level Common-Near-Neighbor Analysis for Multiple-shot Person Re-identification 査読あり
Wei Li, Yang Wu, Yasutomo Kawanishi,Masayuki Mukunoki, Michihiko Minoh
IAPR International Conference on Machine Vision Application(MVA2013) 2013年5月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Combined Object Detection and Segmentation 査読あり
Jarich Vansteenberge, Masayuki Mukunoki, Michihiko Minoh
International Journal of Machine Learning and Computing(IJMLC), Vol.3(1), pp.60-64, DOI: 10.7763/IJMLC.2013.V3.273, ISSN: 2010-3700 2013年2月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Coupled metric learning for single-shot versus single-shot person reidentification 査読あり
Li, Wei; Wu, Yang; Mukunoki, Masayuki; Minoh, Michihiko
OPTICAL ENGINEERING 52 ( 2 ) 2013年2月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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Locality based discriminative measure for multiple-shot person re-identification 査読あり
Li W., Wu Y., Mukunoki M., Minoh M.
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 312 - 317 2013年
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Multiple-shot person re-identification tackles the problem to build the correspondences between sets of human images obtained from distributed cameras. It is challenging due to large within-class variations and small between-class differences, caused by the changing of human appearance and environment. Existing methods for addressing this issue include designing the representation to capture the within-set correlation, or crafting the measure to explore the between-set separation. This paper proposes a novel set based matching model called 'Locality Based Discriminative Measure (LBDM)', in which the discriminative potentiality of a new set-to-set distance is exploited by using the learned local metric field. As experimentally demonstrated, the proposal remarkably outperforms state-of-the-art schemes on public benchmark datasets. © 2013 IEEE.
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Locality-constrained collaborative sparse approximation for multiple-shot person re-identification 査読あり
Wu Y., Mukunoki M., Minoh M.
Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 140 - 144 2013年
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Person re-identification is becoming a hot research topic due to its academic importance and attractive applications in visual surveillance. This paper focuses on solving the relatively harder and more importance multiple-shot re-identification problem. Following the idea of treating it as a set-based classification problem, we propose a new model called Locality-constrained Collaborative Sparse Approximation (LCSA) which is made to be as efficient, effective and robust as possible. It improves the very recently proposed Collaborative Sparse Approximation (CSA) model by introducing two types of locality constraints to enhance the quality of the data for collaborative approximation. Extensive experiments demonstrate that LCSA is not only much better than CSA in terms of effectiveness and robustness, but also superior to other related methods. © 2013 IEEE.
DOI： 10.1109/ACPR.2013.14
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Robust object recognition via third-party collaborative representation 査読あり
Wu Y., Minoh M., Mukunoki M., Lao S.
Proceedings - International Conference on Pattern Recognition 3423 - 3426 2012年12月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Proceedings - International Conference on Pattern Recognition
A simple and effective method is proposed for object recognition via collaborative representation with ridge regression. Different from existing sparse representation and collaborative representation based approaches, the proposal does not need extensive training samples for each testing class and it is robust to localization errors and large within-class variations, thus being applicable to various real-world object recognition tasks instead of handling only the well-controlled face recognition problem. Its discriminative power is explored from a third-party dataset which can be different from the training and testing datasets, therefore, it enables using an existing dictionary for testing new data without time-consuming data annotation and model re-training. As an example, the proposal is extensively tested on the representative and very challenging task of person re-identification, defining novel state-of-the-art results on widely adopted benchmark datasets using only simple and common features. © 2012 ICPR Org Committee.
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Common-near-neighbor analysis for person re-identification 査読あり
Li W., Wu Y., Mukunoki M., Minoh M.
Proceedings - International Conference on Image Processing, ICIP 1621 - 1624 2012年12月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Proceedings - International Conference on Image Processing, ICIP
Person re-identification tackles the problem whether an observed person of interest reappears in a network of cameras. The difficulty primarily originates from few samples per class but large amounts of intra-class variations in real scenarios: illumination, pose and viewpoint changes across cameras. So far, proposals in the literature have treated this either as a matching problem focusing on feature representation or as a classification/ranking problem relying on metric optimization. This paper presents a new way called Common-Near-Neighbor Analysis, which to some extent combines the strengths of these two methodologies. It analyzes the commonness of the near neighbors of each pair of samples in a learned metric space, measured by a novel rank-order based dissimilarity. Our method, using only color cue, has been tested on widely-used benchmark datasets, showing significant performance improvement over the state-of-the-art. © 2012 IEEE.
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Collaborative sparse approximation for multiple-shot across-camera person re-identification 査読あり
Wu Y., Minoh M., Mukunoki M., Li W., Lao S.
Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 209 - 214 2012年11月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012
In this paper we propose a simple and effective solution to the important and challenging problem of acrosscamera person re-identification. We focus on the common case in video surveillance where multiple images or video frames are available for each person. Instead of exploring new features, the proposed approach aims at making a better use of such images/frames. It builds a collaborative representation over all the gallery images (of known person individuals) to best approximate the query images (containing an unknown person) via affine combinations. The approximation is measured by the nearest point distance between the two affine hulls constructed by the query images and gallery images, respectively. By enforcing the sparsity of the samples used for approximating the two nearest points, the relative importance of the gallery images belonging to different persons has the ability to reveal the identity of the querying person. Extensive experiments on public benchmark datasets demonstrate that the proposed approach greatly outperforms the state-of-the-art methods. © 2012 IEEE.
DOI： 10.1109/AVSS.2012.21