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Title: Robust color invariant model for person re-identification
Authors: Chen, Y
Zhao, C
Wang, X
Gao, C
Keywords: Color invariant
Feature representation
Metric learning
Person re-identification
Issue Date: 2016
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2016, v. 9967, p. 695-702 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Person re-identification in a surveillance video is a challenging task because of wide variations in illumination, viewpoint, pose, and occlusion. In this paper, from feature representation and metric learning perspectives, we design a robust color invariant model for person re-identification. Firstly, we propose a novel feature representation called Color Invariant Feature (CIF), it is robust to illumination and viewpoint changes. Secondly, to learn a more discriminant metric for matching persons, XQDA metric learning algorithm is improved by adding a clustering step before computing metric, the new metric learning method is called Multiple Cross-view Quadratic Discriminant Analysis (MXQDA). Experiments on two challenging person re-identification datasets, VIPeR and CUHK1, show that our proposed approach outperforms the state of the art.
Description: 11th Chinese Conference on Biometric Recognition, CCBR 2016, Chengdu, China, 14-16 October 2016
ISBN: 9783319466538
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-46654-5_76
Appears in Collections:Conference Paper

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