ML Anthology

Graphical Models for Graph Matching

Tibério S. Caetano, Terry Caelli, Dante Augusto Couto Barone
CVPR 2004 pp. 466-473
doi:10.1109/CVPR.2004.118 /cvpr/2004/caetano2004cvpr-graphical/

Abstract

This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov random field. We approximate the fully connected model with simpler models in which optimal inference is feasible, and contrast them to the well-known probabilistic relaxation method, which can operate over the complete model but does not assure global optimality. The approach is well suited for applications in which there is redundancy in the binary attributes of the graph, such as in the matching of straight line segments. Results demonstrate that, in this application, the proposed models have superior robustness over probabilistic relaxation under additive noise conditions.

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Cite

Text

Caetano et al. "Graphical Models for Graph Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.118

Markdown

[Caetano et al. "Graphical Models for Graph Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/caetano2004cvpr-graphical/) doi:10.1109/CVPR.2004.118

BibTeX

@inproceedings{caetano2004cvpr-graphical,
  title     = {{Graphical Models for Graph Matching}},
  author    = {Caetano, Tibério S. and Caelli, Terry and Barone, Dante Augusto Couto},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {466-473},
  doi       = {10.1109/CVPR.2004.118},
  url       = {https://mlanthology.org/cvpr/2004/caetano2004cvpr-graphical/}
}