Multi-directional saliency metric learning for person re-identification

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.