Graph Matching with Anchor Nodes: A Learning Approach
This work addresses graph matching for applications like image analysis, but it is incremental as it builds on existing node signature techniques.
The paper tackles the weighted graph matching problem with known anchor node correspondences by formulating an optimization problem to learn a proximity measure from node signatures, then solving an integer quadratic program for matching. Experiments show superior performance on random graphs and image sequences compared to existing methods.
In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes. Our construction exploits recently introduced node signatures based on graph Laplacians, namely the Laplacian family signature (LFS) on the nodes, and the pairwise heat kernel map on the edges. In this paper, without assuming an explicit form of parametric dependence nor a distance metric between node signatures, we formulate an optimization problem which incorporates the knowledge of anchor nodes. Solving this problem gives us an optimized proximity measure specific to the graphs under consideration. Using this as a first order compatibility term, we then set up an integer quadratic program (IQP) to solve for a near optimal graph matching. Our experiments demonstrate the superior performance of our approach on randomly generated graphs and on two widely-used image sequences, when compared with other existing signature and adjacency matrix based graph matching methods.