Due to the high cost of data annotation in supervised person re-identification (re-ID) methods, unsupervised methods become more attractive in the real world. Recently, the hierarchical clustering serves as a promising unsupervised method. One key factor of hierarchical clustering is the distance measurement strategy. Ideally, a good distance measurement should consider both inter-cluster and intra-cluster distance of all samples. To solve this problem, we propose to use the energy distance to measure inter-cluster distance in hierarchical clustering (E-cluster), and use the sum of squares of deviations (SSD) as a regularization term to measure intra-cluster distance for further performance promotion. We evaluate our method on Market-1501 and DukeMTMC-reID. Extensive experiments show that E-cluster obtains significant improvements over the state-of-the-arts fully unsupervised methods.

由于有监督人员重新识别（re-ID）方法中数据注释的成本很高，因此无监督方法在现实世界中变得更具吸引力。最近，分层聚类是一种很有前途的无监督方法。层次聚类的关键因素之一是距离测量策略。理想情况下，良好的距离测量应同时考虑所有样本的群集间和群集内距离。为了解决这个问题，我们建议使用能量距离来测量分层聚类（E聚类）中的聚类间距离，并使用偏差平方和（SSD）作为正则化项来度量聚类内距离以进一步绩效提升。我们在Market-1501和DukeMTMC-reID上评估我们的方法。