Person re-identification by enhanced local maximal occurrence representation and generalized similarity metric learning.

To solve the challenging person re-identification problem, great efforts have been devoted to feature representation and metric learning. However, existing feature extractors are either stripe-based or dense-block-based, the fine details and coarse appearance are not well integrated. What is more, the metrics are generally learned independently from distance view or bilinear similarity view. Few works have exploited the mutual complementary effects of their combination. To address these issues, we propose a new feature representation termed enhanced Local Maximal Occurrence (eLOMO) which fuses a new overlapping-stripe-based descriptor with the Local Maximal Occurrence (LOMO) extracted from dense blocks. Such integration makes eLOMO resemble the coarse-to-fine recognition mechanism of human vision system, thus it can provide a more discriminative descriptor for re-identification. Besides, we show the advantages of learning generalized similarity by combining the Mahalanobis distance and bilinear similarity together. Specifically, we derive a logistic metric learning method to jointly learn a distance metric and a bilinear similarity metric, which exploits both the distance and angle information from training data. Taking advantage of learning in the intra-class subspace, the proposed method can be solved efficiently by coordinate descent optimization. Experiments on four challenging datasets including VIPeR, PRID450S, QMUL GRID, and CUHK01, show that the proposed method outperforms the state-of-the-art approaches significantly.