Person re-identification with dictionary learning regularized by stretching regularization and label consistency constraint

Abstract

Person re-identification (PRID) is a rather challenging task due to the ambiguity of visual appearance. In this paper, we develop a dictionary-based projection transformation learning approach, where the idea of metric learning and dictionary learning are introduced into a unified framework to make full use of their respective advantages. More specifically, to cope with the challenge caused by dramatic changes in visual appearance, we first project the image features of pedestrian into a discriminative subspace to make the same person from different views with the same coding coefficients. Moreover, we develop a new stretch regularization to make the distance between different pedestrian images larger than that of the same pedestrian images so as to reduce the similarity exhibited by different pedestrian images. Additionally, we develop a label consistency constraint and integrate it into the dictionary learning and then we obtain the ensemble learning model of identity discriminator and dictionary. As a result, the coding coefficient and the corresponding label are bridged and the supervision from the labeled samples is also better exploited. Experimental results on five popular person re-identification benchmarks indicate that the approach developed in this paper has higher identification performance than some state-of-the-art methods.