Nonparametric Image Clustering Based on Variational Bayesian Contrastive Network
ZHANG Shengjie1, WANG Yifei1, XIANG Wang1, XUE Dizhan2, QIAN Shengsheng2
1. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450003; 2. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract：The number of clusters in nonparametric image clustering is unknown and it needs to be discovered by the model automatically. Although some existing Bayesian methods can automatically infer the number of clusters, they are not feasible on large-scale image datasets due to the high computational costs or over-reliance on learned features. Therefore, nonparametric image clustering based on variational Bayesian contrastive network is proposed in this paper. Firstly, image features are extracted by ResNet. Secondly, deep variational Dirichlet process mixture is put forward to automatically infer the number of clusters, and it can be directly embedded into end-to-end deep models and jointly optimized with feature extractors. Finally, polarized contrast clustering learning is presented, and the denoising strategy with polarized label is utilized to denoise and polarize the labels. The polarized labels and data augmented predicted labels are employed for comparative learning to jointly optimize image feature extractors and clustering model. Experiments on three benchmark datasets show that the performance of the proposed method is superior.
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2023, Vol. 36 
