Combining Feature Context and Spatial Context for Image Pattern Discovery
Once an image is decomposed into a number of visual primitives, e.g., local interest points or salient image regions, it is of great interests to discover meaningful visual patterns from them. Conventional clustering (e.g., k-means) of visual primitives, however, usually ignores the spatial dependency among them, thus cannot discover the high-level visual patterns of complex spatial structure. To overcome this problem, we propose to consider both spatial and feature contexts among visual primitives for pattern discovery. By discovering both spatial co-occurrence patterns among visual primitives and feature co-occurrence patterns among different types of features, our method can better handle the ambiguities of visual primitives, by leveraging these co-occurrences. We formulate the problem as a regularized k-means clustering, and propose an iterative bottom-up/top-down self-learning procedure to gradually refine the result until it converges. The experiments of image text on discovery and image region clustering convince that combining spatial and feature contexts can significantly improve the pattern discovery results.