  Due to the high cost of data annotation in supervised learning for person
re-identification (Re-ID) methods, unsupervised learning becomes more
attractive in the real world. The Bottom-up Clustering (BUC) approach based on
hierarchical clustering serves as one promising unsupervised clustering method.
One key factor of BUC is the distance measurement strategy. Ideally, the
distance measurement should consider both inter-cluster and intra-cluster
distance of all samples. However, BUC uses the minimum distance, only considers
a pair of the nearest sample between two clusters and ignores the diversity of
other samples in clusters. To solve this problem, we propose to use the energy
distance to evaluate both the inter-cluster and intra-cluster distance in
hierarchical clustering(E-cluster), and use the sum of squares of
deviations(SSD) as a regularization term to further balance the diversity and
similarity of energy distance evaluation. We evaluate our method on large scale
re-ID datasets, including Market-1501, DukeMTMC-reID and MARS. Extensive
experiments show that our method obtains significant improvements over the
state-of-the-art unsupervised methods, and even better than some transfer
learning methods.
