Common Visual Pattern Discovery via Spatially Coherent Correspondences

We investigate how to discover all common visual patterns within two sets of feature points. Common visual patterns generally share similar local features as well as similar spatial layout. In this paper these two types of information are integrated and encoded into the edges of a graph whose nodes represent potential correspondences, and the common visual patterns then correspond to those strongly connected subgraphs. All such strongly connected subgraphs correspond to large local maxima of a quadratic function on simplex, which is an approximate measure of the average intra-cluster affinity score of these subgraphs. We find all large local maxima of this function, thus discover all common visual patterns and recover the correct correspondences, using replicator equation and through a systematic way of initialization. The proposed algorithm possesses two characteristics: 1) robust to outliers, and 2) being able to discover all common visual patterns, no matter the mappings among the common visual patterns are one to one, one to many, or many to many. Extensive experiments on both point sets and real images demonstrate the properties of our proposed algorithm in terms of robustness to outliers, tolerance to large spatial deformations, and simplicity in implementation.