Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes

Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki

ICCV 2013

doi:10.1109/ICCV.2013.168 /iccv/2013/zhang2013iccv-learning-a/

Abstract

Although graph matching is a fundamental problem in pattern recognition, and has drawn broad interest from many fields, the problem of learning graph matching has not received much attention. In this paper, we redefine the learning of graph matching as a model learning problem. In addition to conventional training of matching parameters, our approach modifies the graph structure and attributes to generate a graphical model. In this way, the model learning is oriented toward both matching and recognition performance, and can proceed in an unsupervised gnfashion. Experiments demonstrate that our approach outperforms conventional methods for learning graph matching.

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Cite

Text

Zhang et al. "Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.168

Markdown

[Zhang et al. "Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/zhang2013iccv-learning-a/) doi:10.1109/ICCV.2013.168

BibTeX

@inproceedings{zhang2013iccv-learning-a,
  title     = {{Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes}},
  author    = {Zhang, Quanshi and Song, Xuan and Shao, Xiaowei and Zhao, Huijing and Shibasaki, Ryosuke},
  booktitle = {International Conference on Computer Vision},
  year      = {2013},
  doi       = {10.1109/ICCV.2013.168},
  url       = {https://mlanthology.org/iccv/2013/zhang2013iccv-learning-a/}
}