Consistent Iterative Multi-view Transfer Learning for Person Re-identification

Inconsistent data distributions among multiple views is one of the most crucial aspects of person re-identification. To solve the problem, this paper presents a novel strategy called consistent iterative multi-view transfer learning model. The proposed model captures seven groups of multi-view visual words (MvVW) through an unsupervised cluster method (K-means) from human body. For each group of MvVW, a multi-view discriminative common subspace can be obtained by the fusion of transfer learning and discriminative analysis. In these common subspaces, the original samples can be reconstructed based on MvVW under the low-rank and sparse constraints. Then, we solve it via the inexact augmented Lagrange multiplier method. The proposed strategy is performed on three different challenging person re-identification databases (i.e., VIPeR, CUHK01 and PRID450S), which shows that our model outperforms several state-of-the-art models with improving of 6.36%, 7.7% and 4.0% respectively.