A Bottom Up Clustering Approach to Unsupervised Person Re Identification
Most person re identification (re ID) approaches are based on supervised learning, which requires intensive manual annotation for training data. However, it is not only resourceintensive to acquire identity annotation but also impractical to label the large scale real world data. To relieve this problem, we propose a bottom up clustering (BUC) approach to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples. Our algorithm considers two fundamental facts in the re ID task, i.e. diversity across different identities and similarity within the same identity. Specifically, our algorithm starts with regarding individual sample as a different identity, which maximizes the diversity over each identity. Then it gradually groups similar samples into one identity, which increases the similarity within each identity. We utilizes a diversity regularization term in the bottom up clustering procedure to balance the data volume of each cluster. Finally, the model achieves an effective trade off between the diversity and similarity. We conduct extensive experiments on the large scale image and video re ID datasets, including Market 1501, DukeMTMCreID, MARS and DukeMTMC VideoReID. The experimental results demonstrate that our algorithm is not only superior to state of the art unsupervised re ID approaches, but also performs favorably than competing transfer learning and semi supervised learning methods.
AAAI