10 April 2018 Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150Q (2018) https://doi.org/10.1117/12.2303485
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearancebased features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.
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You He, You He, Song Wu, Song Wu, Nan Pu, Nan Pu, Li Qian, Li Qian, Guoqiang Xiao, Guoqiang Xiao, } "Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150Q (10 April 2018); doi: 10.1117/12.2303485; https://doi.org/10.1117/12.2303485
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