Mining And-or Graphs for Graph Matching and Object Discovery

Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu

ICCV 2015

doi:10.1109/ICCV.2015.15 /iccv/2015/zhang2015iccv-mining/

Abstract

This paper reformulates the theory of graph mining on the technical basis of graph matching, and extends its scope of applications to computer vision. Given a set of attributed relational graphs (ARGs), we propose to use a hierarchical And-Or Graph (AoG) to model the pattern of maximal-size common subgraphs embedded in the ARGs, and we develop a general method to mine the AoG model from the unlabeled ARGs. This method provides a general solution to the problem of mining hierarchical models from unannotated visual data without exhaustive search of objects. We apply our method to RGB/RGB-D images and videos to demonstrate its generality and the wide range of applicability. The code will be available at https://sites.google.com/site/quanshizhang/mining-and-or-graphs.

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Cite

Text

Zhang et al. "Mining And-or Graphs for Graph Matching and Object Discovery." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.15

Markdown

[Zhang et al. "Mining And-or Graphs for Graph Matching and Object Discovery." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/zhang2015iccv-mining/) doi:10.1109/ICCV.2015.15

BibTeX

@inproceedings{zhang2015iccv-mining,
  title     = {{Mining And-or Graphs for Graph Matching and Object Discovery}},
  author    = {Zhang, Quanshi and Wu, Ying Nian and Zhu, Song-Chun},
  booktitle = {International Conference on Computer Vision},
  year      = {2015},
  doi       = {10.1109/ICCV.2015.15},
  url       = {https://mlanthology.org/iccv/2015/zhang2015iccv-mining/}
}