A Graph-based Image Clustering Method Using Mutual Information Maximization
Xun Bo Yu, Zicheng Pan, Yongsheng Gao · 2019
In this paper, we present a novel image clustering method that converts a typical image clustering problem into a graph node classification task, given only unlabelled data samples. Graph convolutional network is utilized to perform graph representation encoding. Unlike traditional clustering approaches relying on hand-crafted criteria, our method learns to cluster graph-structured data by maximizing mutual information between the global-graph representation and local-graph representations. The learnt graph embeddings can preserve global information in all locations of local-graphs such as pseudo labels for image clustering tasks. To test the generalization and robustness of the proposed method, we conduct experiments on two different benchmarks, such as fashion classification and finegrained object classification. The preliminary experiment results show that the proposed method outperforms all the baselines.