NDLI: Large margin relative distance learning for person re-identification

Content Provider	IET Digital Library
Author	Dong, Husheng Gong, Shengrong Liu, Chunping Ji, Yi Zhong, Shan
Abstract	Distance metric learning has achieved great success in person re-identification. Most existing methods that learn metrics from pairwise constraints suffer the problem of imbalanced data. In this study, the authors present a large margin relative distance learning (LMRDL) method which learns the metric from triplet constraints, so that the problem of imbalanced sample pairs can be bypassed. Different from existing triplet-based methods, LMRDL employs an improved triplet loss that enforces penalisation on the triplets with minimal inter-class distance, and this leads to a more stringent constraint to guide the learning. To suppress the large variations of pedestrian's appearance in different camera views, the authors propose to learn the metric over the intra-class subspace. The proposed method is formulated as a logistic metric learning problem with positive semi-definite constraint, and the authors derive an efficient optimisation scheme to solve it based on the accelerated proximal gradient approach. Experimental results show that the proposed method achieves state-of-the-art performance on three challenging datasets (VIPeR, PRID450S, and GRID).
Starting Page	455
Ending Page	462
Page Count	8
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 6, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0265
Journal	IET Computer Vision
Publisher Date	2017-04-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Distance Metric Learning Gradient Method Image Matching Image Recognition Imbalanced Data Imbalanced Sample Pairs Knowledge Engineering Technique Large Margin Relative Distance Learning Learning in AI LMRDL Logistic Metric Learning Problem Optimisation Scheme Optimisation Technique Pedestrian Person Reidentification Proximal Gradient Approach Triplet Constraint
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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