Unsupervised Person Re-Identification Using Multi-Branch Feature Compensation Network And Link-Based Cluster Dissimilarity Metric

Feature extraction and label estimation are critical in unsupervised person re-identification (re-ID). Most previous works focus on acquiring high-layer semantic features and reckon without the lower-layer details lost in the learning process, which causes the extracted features to be less comprehensive and may degrade re-ID performance. Therefore in this paper, a Multi-branch Feature Compensation Network (MFC-Net) is developed in which the significant parts of lower-layer features are learned and fused with high-layer feature as compensation. Moreover, to accurately conduct label estimation through applying hierarchical clustering on individual samples, a Link-based Cluster Dissimilarity Metric (LCDM) is proposed to excavate the inner correlation between clusters. These two methods are later integrated as a MFC-LCDM scheme to improve unsupervised re-ID performance. Extensive experiments on large-scale image-based and video-based datasets ulteriorly demonstrate the superiority and effectiveness of MFC-LCDM.