Mining And-Or Graphs for Graph Matching and Object Discovery
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.