Multiple-Shot People Re-Identification by Patch-Wise Learning

Guanwen ZHANG  Jien KATO  Yu WANG  Kenji MASE  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.12   pp.2257-2270
Publication Date: 2015/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDP7427
Type of Manuscript: PAPER
Category: Pattern Recognition
patch-wise matching,  patch-wise learning,  multiple-shot re-identification,  

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In this paper, we propose a patch-wise learning based approach to deal with the multiple-shot people re-identification task. In the proposed approach, re-identification is formulated as a patch-wise set-to-set matching problem, with each patch set being matched using a specifically learned Mahalanobis distance metric. The proposed approach has two advantages: (1) a patch-wise representation that moderates the ambiguousness of a non-rigid matching problem (of human body) to an approximate rigid one (of body parts); (2) a patch-wise learning algorithm that enables more constraints to be included in the learning process and results in distance metrics of high quality. We evaluate the proposed approach on popular benchmark datasets and confirm its competitive performance compared to the state-of-the-art methods.