Self-supervised Multi-view Clustering for Unsupervised Image Segmentation

At present, the main idea of CNN-based unsupervised image segmentation is clustering a single image in the framework of CNNs. However, the single image clustering is very difficult to obtain enough supervision information for network learning. For solving this problem, we propose a Self-supervised Multi-view Clustering (SMC) structure for unsupervised image segmentation to mine additional supervised information. Based on the observation that the predicted pixel-level labels and the input images have the same spatial features, the multi-view images acquired by data augmentation are clustered to obtain the multi-view results and the proposed SMC uses the differences among these results to learn self-supervised information. Moreover, a Hybrid Self-supervised (HS) loss is proposed to make full use of the self-supervised information for further improving the prediction accuracy and the convergence speed. Extensive experiments in BSD500 and PASCAL VOC 2012 datasets demonstrate the superiority of our proposed approach.