Open AccessMulti‐directional saliency metric learning for person re‐identification
Author(s) -
Chen Ying,
Huo Zhonghua,
Hua Chunjian
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0343
Subject(s) - salience (neuroscience) , discriminative model , artificial intelligence , computer science , pattern recognition (psychology) , metric (unit) , support vector machine , similarity (geometry) , matching (statistics) , consistency (knowledge bases) , computer vision , machine learning , mathematics , image (mathematics) , statistics , engineering , operations management
A multi‐directional salience based similarity evaluation for person re‐identification (re‐id) is presented. After distribution analysis for salience consistency between image pairs, a similarity between matched patches is established by weighted fusion of multi‐directional salience. The weight of saliency in each direction is obtained using metric learning by means of structural support vector machines ranking. The discriminative and accurate performance of re‐id is achieved. Compared with existing salience based person matching framework, the proposed method achieves higher re‐id rate with multi‐directional salience based similarity evaluation.