Subgraph matching algorithm based on graduated nonconvexity and concavity procedure

To achieve robust and efficient matching with outliers is a fundamental problem in the field of graph matching. To tackle this problem, a novel subgraph matching algorithm was proposed, which was based on the recently proposed graduated nonconvexity and concavity procedure (GNCCP). Specifically speaking, the graph matching problem in the existence of outliers was firstly formulated as a quadratic combinatorial optimization problem based on the affinity matrix, which was then optimized by extending the GNCCP. This is a new second-order constraint graph matching algorithm. Compared with the existing algorithms, there are mainly three benefits for the proposed algorithm, which are as follows. Firstly, it has a flexible objective function formulation; secondly, it is effective in graph matching problems with outliers; thirdly, it is applicable on both directed graphs and undirected graphs. Simulations on both synthetic and real world datasets validate the effectiveness of the proposed method. ©, 2015, Beijing University of Aeronautics and Astronautics (BUAA). All right reserved.