Similarity learning with joint transfer constraints for person re-identification

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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.