Common-near-neighbor analysis for person re-identification
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...
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Published in | 2012 19th IEEE International Conference on Image Processing pp. 1621 - 1624 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2012
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Abstract | 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. |
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AbstractList | 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. |
Author | Yang Wu Mukunoki, M. Minoh, M. Wei Li |
Author_xml | – sequence: 1 surname: Wei Li fullname: Wei Li organization: Grad. Sch. of Inf., Kyoto Univ., Kyoto, Japan – sequence: 2 surname: Yang Wu fullname: Yang Wu organization: Acad. Center for Comput. & Media Studies, Kyoto Univ., Kyoto, Japan – sequence: 3 givenname: M. surname: Mukunoki fullname: Mukunoki, M. organization: Acad. Center for Comput. & Media Studies, Kyoto Univ., Kyoto, Japan – sequence: 4 givenname: M. surname: Minoh fullname: Minoh, M. organization: Acad. Center for Comput. & Media Studies, Kyoto Univ., Kyoto, Japan |
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Snippet | Person re-identification tackles the problem whether an observed person of interest reappears in a network of cameras. The difficulty primarily originates from... |
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SubjectTerms | Cameras common-nearneighbor analysis Educational institutions Extraterrestrial measurements Lighting metric learning Person re-identification Support vector machines Surveillance |
Title | Common-near-neighbor analysis for person re-identification |
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