Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

You He; Song Wu; Nan Pu; Li Qian; Guoqiang Xiao

doi:10.1117/12.2303485

10 April 2018 Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

You He, Song Wu, Nan Pu, Li Qian, Guoqiang Xiao

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.

Citation Download Citation

You He, Song Wu, Nan Pu, Li Qian, and 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); https://doi.org/10.1117/12.2303485

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KEYWORDS

Associative arrays

Cameras

Feature extraction

Dielectrophoresis

Visual process modeling

Computer vision technology

Performance modeling

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