Similarity learning with joint transfer constraints for person re-identification

The inconsistency of data distributions among multiple views is one of the most crucial issues which hinder the accuracy of person re-identification. To solve the problem, this paper presents a novel similarity learning model by combining the optimization of feature representation via multi-view visual words reconstruction and the optimization of metric learning via joint discriminative transfer learning. The starting point of the proposed model is to capture multiple groups of multi-view visual words (MvVW) through an unsupervised clustering method (i.e. K-means) from human parts (e.g. head, torso, legs). Then, we construct a joint feature matrix by combining multi-group feature matrices with different body parts. To solve the inconsistent distributions under different views, we propose a method of joint transfer constraint to learn the similarity function by combining multiple common subspaces, each in charge of a sub-region. In the common subspaces, the original samples can be reconstructed based on MvVW under low-rank and sparse representation constraints, which can enhance the structure robustness and noise resistance. During the process of objective function optimization, based on confinement fusion of multi view and multiple sub-regions, a solution strategy is proposed to solve the objective function using joint matrix transform. Taking all of these into account, the issue of person re-identification under inconsistent data distributions can be transformed into a consistent iterative convex optimization problem, and solved via the inexact augmented Lagrange multiplier (IALM) algorithm. Extensive experiments are conducted on three challenging person re-identification datasets (i.e., VIPeR, CUHK01 and PRID450S), which shows that our model outperforms several state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.