DIVERSITY REGULARIZED METRIC LEARNING FOR PERSON RE-IDENTIFICATION

Metric learning is an effective method for person re-identification. It utilizes latent factors to find a suitable space for measuring distances. In general, a small number of factors are not powerful enough to match the pedestrians while a large number of factors cause high computational cost. In this paper, to balance this trade-off, a novel diversity regularized distance metric learning method is proposed. For feature representation, the local discriminative features are extracted from the source image and an adjacency maximal constraint is developed to handle the misaligned issue. Then a diversity regularizer is used to learn a metric by making the latent factors uncorrelated so that a small amount of latent factors can preserve effectiveness in measuring distances while reducing the computational burden. Our experimental results show that the proposed method with a small amount of factors can obtain comparative or even better performance compared to the state-of-art methods.