A Multi-level Equilibrium Clustering Approach for Unsupervised Person Re-identification

Unsupervised person re-identification (re-ID) has not achieved desired results because learning a discriminative feature embedding without annotation is difficult. Fortunately, the special distribution of samples in this task provides critical priority information for addressing this problem. On the one hand, the distribution of samples belonging to the same identity is multi-centered. On the other hand, distribution is distinct for samples of different levels that cropped from the images. According to the first property, we propose the equilibrium criterion, which provides a suitable measurement of dissimilarity between samples around a center or that from different centers. According to the second property, we introduce multi-level labels guided learning to mine and utilize the complementary information among different levels. Extensive experiments demonstrate that our method is superior to the state-of-the-art unsupervised re-ID approaches in significant margins.