Mining And-Or Graphs for Graph Matching and Object Discovery
Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu
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.
BibTeX
@inproceedings{iccv2015_miningandorgraph,
title = {Mining And-Or Graphs for Graph Matching and Object Discovery},
author = {Quanshi Zhang and Ying Nian Wu and Song-Chun Zhu},
booktitle = {ICCV 2015},
year = {2015}
}