Relevance metric learning for person re-identification by exploiting global similarities

Person re-identification aims to match people across non-overlapping camera views, which is an important and challenging task. In order to obtain a robust metric for measuring (dis)similarities of (un)matched image pairs, metric learning has been introduced recently. Most existing works focus on seeking a Mahalanobis distance by employing sparse pair wise (dis)similarity constraints. However, the pair wise constraints have ignored a large portion of useful similarity information, and could not provide global similarity information. This paper proposes a novel metric learning method that could effectively exploit the global similarities. Specifically, we predefine lists of similarity scores, and measure (dis)similarities by the relevance of feature vectors. Subsequently, we learn a relevance metric by using the predefined list wise constraints, where the learnt metric is enforced to conserve predefined list wise similarities. Our main contributions lie on three folds: (1) we propose a metric learning method, which could effectively encode the global similarity information by using list wise constraints, (2) we formulate the relevance metric learning into a convex optimization problem, which could be solved efficiently, (3) we further kernelize the proposed method to support nonlinear mappings. The proposed method is experimentally validated on benchmark datasets, and outperforms state-of-the-art metric learning methods.