Density Division Face Clustering Based on Graph Convolutional Networks

Supervised clustering methods cluster images using graph convolutional networks (GCN) via linkage prediction, and have shown significant improvements over the traditional clustering algorithms (e.g., K-means, DBScan, etc.) in terms of clustering effectiveness. However, existing supervised clustering approaches are always time-consuming, which may limit their usage. The high computation overhead is mainly resulted from generating and processing a large amount of subgraphs, each of which is generated for one image instance in order to infer the linkage between them. To tackle the high computation problem, we propose a new density division clustering approach based on GCN, and our experiments demonstrate that the new approach is both time-efficient and effective. The approach divides the data into high-density and low-density parts, and only performs GCN subgraph link inference on the low-density parts, which highly reduces redundant calculations. Meanwhile, to ensure sufficient contextual information extraction for low-density parts, it generates adaptive subgraphs instead of fixed-size subgraphs. Our experimental evaluations over multiple datasets show that our proposed approach is five-time faster than state-of-the-art algorithms with even higher accuracy.