Self-paced least square semi-coupled dictionary learning for person re-identification

Abstract

Person re-identification aims to match people across disjoint camera views. It has been reported that Least Square Semi-Coupled Dictionary Learning (LSSCDL) based sample-specific SVM learning framework has obtained the state of the art performance. However, the objective function of the LSSCDL, the algorithm of learning the pairs (feature, weight) dictionaries and the mapping function between feature space and weight space, is non-convex, which usually result in suboptimal solutions with the bad local minima of the objective function. To tackle with this constraint, we present Self-Paced Least Square Semi-Coupled Dictionary Learning (SLSSCDL) algorithm, which is inspired by previous works on self-paced learning, a framework able to improve the accuracy of conventional learning models by presenting the training data in a meaningful order to get a better local minima, i.e. easy samples are provided first. In addition, a graph based regularization term is also introduced to preserve the local similarities in each space. Experimental results show that SLSSCDL gains competitive performance on two challenging datasets.