Learning Mid-level Filters for Person Re-identification

Rui Zhao      Wanli Ouyang      Xiaogang Wang

The Chinese University of Hong Kong

intro figure 1

Figure 1. Three types of patches with different discriminative and generalization powers. Each dashed box indicates a patch cluster. To make filters region-specific, we cluster patches within the same horizontal stripe across different pedestrian images. See details in the text of Section 1 of our paper.

intro figure 1

Figure 2. Filter in (a1 )(b1 ) is learned from a cluster with incoherent appearance and generates scattered responses in the two images. Filter in (a2)(b2) is learned from a cluster with coherent appearance. It generates compact responses. It also has view-invariance. It matches (a2) and (b2) which are the same person in different views, while distinguishes (b2) and (b'2) which are different person in the same view.


In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.