NDLI: Learning Mid-level Filters for Person Re-identification

Content Provider	IEEE Xplore Digital Library
Author	Rui Zhao Wanli Ouyang Xiaogang Wang
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Rui Zhao; Wanli Ouyang; Xiaogang Wang)
Abstract	In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.
Starting Page	144
Ending Page	151
File Size	3014237
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Visualization Training Cameras Robustness Quantization (signal) Matched filters person re-identification Mid-level filter
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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