Robust image clustering via context-aware contrastive graph learning

Abstract

Graph convolution networks (GCN) have recently become popular for image clustering. However, existing GCN-based image clustering techniques focus on learning image neighbourhoods which leads to poor reasoning on the cluster boundaries. To address this challenge, we propose a supervised image clustering approach based on contrastive graph learning (CGL). Our method generates an influential graph view (IGV) and a topological graph view (TGV) for each class to represent its global context from different viewpoints. These generated graph views are used to reason the inter-cluster relationships and intra-cluster boundaries from the local context of each node in a contrastive manner. Our method considers each class as a fully connected graph to explore its characteristics and strategically generate directional graph views. This enhances the transferability of the proposed approach to handle data with a similar structure. We conduct extensive experiments on open datasets such as LFW, CASIA-WebFace, and CIFAR-10 and show that our method outperforms state-of-the-art including deep GRAph Contrastive rEpresentation learning (GRACE), GraphCL, and Graph Contrastive Clustering (GCC).