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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.

This paper presents a novel image feature representation method, called multi-channel micro-structure difference descriptor (MCMSDD) for image retrieval. With the local feature extraction from a micro-structure and MAX operator, MCMSDD integrates the advantages of multi-channel local binary encoding and color difference histogram , which are the fusion of color, texture and spatial distribution information. Although it extracts feature from full color image, the dimension of the feature vector is relatively low without learning and segmentation. To improve the performance of retrieval, a simple re-ranking algorithm is employed. Finally, the proposed MCMSDD is extensively tested on Corel-2K and Washington datasets, and the experimental results show that the proposed MCMSDD is more effective than the state-of-the-art.