Mining And-Or Graphs for Graph Matching and Object Discovery
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu, in ICCV 2015
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 the 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.
Definitions of mining And-Or Graphs
Results: mining from different visual data
Please contact Dr. Quanshi Zhang, if you have questions.