Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410118 | Neurocomputing | 2013 | 5 Pages |
Exploiting both appearance similarity and geometric consistency is popular in addressing the feature correspondence problem. However, when there exist outliers the performance generally deteriorates greatly. In this paper, we propose a novel partial correspondence method to tackle the problem with outliers. Specifically, a novel pairwise term together with a neighborhood system is proposed, which, together with the other two pairwise terms and a unary term, formulates the correspondence to be solved as a subgraph matching problem. The problem is then approximated by the recently proposed Graduated Non-Convexity and Graduated Concavity Procedure (GNCGCP). The proposed algorithm obtains a state-of-the-art accuracy in the existence of outliers while keeping O(N3)O(N3) computational complexity and O(N2)O(N2) storage complexity. Simulations on both the synthetic and real-world images witness the effectiveness of the proposed method.