Spatial-temporal representatives selection and weighted patch descriptor for person re-identification

How to represent the sequential person images is a crucial issue in multi-shot person re-identification. In this paper, we propose to select the spatial-temporal informative representatives to describe the image sequence. Specifically, we address representatives selection as a row-sparsity regularized minimization problem which can be effectively solved via convex programming. The sparsity of the representatives is controlled by a regularization parameter based on both spatial and temporal dissimilarities. Furthermore, we design a weighted patch descriptor by employing the random walk with restart model to propagate the patch weights on the person image. Finally, we utilize the cross-view quadratic discriminant analysis as the metric learning to mitigate the cross-view gaps among different cameras. Extensive experiments on three benchmark datasets iLIDS-VID, PRID 2011 and SAIVT-SoftBio demonstrate the promising performance of the proposed method.