Mining And-Or Graphs for Graph Matching and Object Discovery

Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 55-63

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.

Related Material


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[bibtex]
@InProceedings{Zhang_2015_ICCV,
author = {Zhang, Quanshi and Nian Wu, Ying and Zhu, Song-Chun},
title = {Mining And-Or Graphs for Graph Matching and Object Discovery},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}