Clique-Graph Matching by Preserving Global & Local Structure

This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise clique correspondence constrained by the one-to-one matching. Since the objective function is not jointly convex to both latent variables, we decompose it into two consecutive steps for optimization: 1) clique-to-clique similarity measure by preserving local unary and pairwise correspondences; 2) graph-to-graph similarity measure by preserving global clique-to-clique correspondence. Extensive experiments on the synthetic data and real images show that the proposed method can outperform representative methods especially when both noise and outliers exist.