Coupled feature spaces learning with joint graph regularization for person re-identification
- Resource Type
- Conference
- Authors
- Bian, Peng; Jin, Yi; Jiang, Luyue; Li, Yidong
- Source
- 2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Behavioral, Economic and Socio-cultural Computing (BESC), 2016 International Conference on. :1-5 Nov, 2016
- Subject
- Computing and Processing
Cameras
Measurement
Learning systems
Iterative methods
Training
Minimization
Feature extraction
re-identification
coupled space
joint graph
- Language
Re-identification of individuals has already drawn growing attentions due to the increasing intelligent visual surveillance. Human signature is quite different over a network of cameras and most related work devotes to selecting human features without any distinction. To address the problem, we propose a novel coupled feature space learning with joint graph regularization in this paper. The proposed method aims to learn a joint graph regularized common feature space in which two projection matrices can be matched. In the procedure, we use l 21 -norm to select relevant and discriminative features from coupled space simultaneously. A joint graph regular term enhances the relevance of different photos from the same person. Comparisons results show the superiority and efficiency of our proposed method with performance measured in terms of Cumulative Match Characteristic curves (CMC) on three challenging datasets.