Heterogeneous Distance Learning Based on Kernel Analysis-Synthesis Dictionary for Semi-Supervised Image to Video Person Re-Identification

Abstract

Image to video person re-identification (IVPR), i.e., matching between pedestrian video and image, is an important task in practice. Although several methods have been presented for IVPR, most of these methods investigate the IVPR problem under the supervised setting, and require a large number of labeled image-video pairs for training. In this article, we study the IVPR problem under the semi-supervised setting, and propose a Kernel Analysis-synthesis Dictionary based heterogeneous Distance Learning (KADDL) approach. Specifically, KADDL first learns two pairs of kernel analysis-synthesis dictionaries from the labeled and unlabeled training image-video data in the kernel space. With the learned dictionary pairs, the heterogeneous image and video features can be transformed into coding coefficients of the same representation space, such that the gap between image and video can be bridged. Then, KADDL learns a discriminative distance metric over the transformed coding coefficients, to make the coding coefficients of positive image-video pair become similar, while those of negative image-video pair dissimilar. To make better use of the unlabeled data, we further designed a reliability-based semi-supervised strategy for KADDL. Experiments on several publicly available pedestrian sequence datasets demonstrate the effectiveness of the proposed approach.