A Weighted Common Subgraph Matching Algorithm
Abstract
We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs. WCS matching, as a natural generalization of the equal-sized graph matching or subgraph matching, finds wide applications in many computer vision and machine learning tasks. In this paper, the WCS matching is first formulated as a combinatorial optimization problem over the set of partial permutation matrices. Then it is approximately solved by a recently proposed combinatorial optimization framework - Graduated NonConvexity and Concavity Procedure (GNCCP). Experimental comparisons on both synthetic graphs and real world images validate its robustness against noise level, problem size, outlier number, and edge density.
Cite
@article{arxiv.1411.0763,
title = {A Weighted Common Subgraph Matching Algorithm},
author = {Xu Yang and Hong Qiao and Zhi-Yong Liu},
journal= {arXiv preprint arXiv:1411.0763},
year = {2014}
}
Comments
6 pages, 5 figures, the second round revision in IEEE TNNLS