Multi‐directional saliency metric learning for person re‐identification

Abstract

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.

Keywords

• multi-directional salience based similarity evaluation
• person reidentification
• distribution analysis
• salience consistency
• image pairs
• weighted fusion