Improved Hierarchical Clustering with Non-locally Enhanced Features for Unsupervised Person Re-identification

Due to the high cost of data annotation in supervised person re-identification (re-ID) methods, unsupervised person re-ID methods have attracted more and more attention. The unsupervised person re-ID methods based on deep clustering have achieved good performance. However, the distance metrics used in existing unsupervised clustering methods ignore intra-cluster distance and are likely to cause some wrong merging situations and uneven distribution within clusters. Besides, these models based on deep clustering usually ignore the importance of global features for person re-ID. In this paper, we address the above problems by proposing an improved hierarchical clustering approach with non-locally enhanced features. To improve the clustering performance, we design a new metric which consists of intermediate distance as inter-cluster distance and compactness degree as intra-cluster distance. The former one can prevent some wrong merging situations and the latter one can promote the uniform distribution within clusters. In addition, we develop a non-locally enhanced feature network to take advantage of global features of images. Extensive experiments on Market-1501, DukeMTMC-reID, MARS and DukeMTMC-VideoReID demonstrate that our method obtains significant improvement over the state-of-the-art unsupervised methods.