Graph Matching via Sequential Monte Carlo
Abstract
Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers.
Cite
Text
Suh et al. "Graph Matching via Sequential Monte Carlo." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33712-3_45Markdown
[Suh et al. "Graph Matching via Sequential Monte Carlo." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/suh2012eccv-graph/) doi:10.1007/978-3-642-33712-3_45BibTeX
@inproceedings{suh2012eccv-graph,
title = {{Graph Matching via Sequential Monte Carlo}},
author = {Suh, Yumin and Cho, Minsu and Lee, Kyoung Mu},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {624-637},
doi = {10.1007/978-3-642-33712-3_45},
url = {https://mlanthology.org/eccv/2012/suh2012eccv-graph/}
}