Person re-identification based on multi-directional saliency metric learning

Abstract

Aiming for the problem of inconsistent saliency between matched patches in person re-identification, a multi-directional salience similarity evaluation for person re-identification based on metric learning is proposed. A distribution analysis for salience consistency between the patches is taken, and the 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 in the base of Structural SVM Ranking. It improves the discriminative and accuracy performance of re-identification. Compared with the similar algorithms, the method achieves higher re-identification rate with more comprehensive similarity measure.