Centralized and Clustered Features for Person Re-identification

Extracting trusted label from unlabeled data and enhancing the discriminative ability of features are essential issues in person re-identification. The latest study shows that the progressive unsupervised learning has a good performance, and its advantage is specially shown in the ability to select reliable samples from the unlabeled dataset by clustering pedestrian features. Since the features ex...