Abstract

Graph matching and graph mining are two typical ar-eas in artificial intelligence. In this paper, we define thesoft attributed pattern (SAP) to describe the common sub-graph pattern among a set of attributed relational graphs(ARGs), considering both the graphical structure and graph attributes. We propose a direct solution to extract the S-AP with the maximal graph size without node enumeration.Given an initial graph template and a number of ARGs, wemodify the graph template into the maximal SAP among theARGs in an unsupervised fashion. The maximal SAP ex-traction is equivalent to learning a graphical model (i.e. an object model) from large ARGs (i.e. cluttered RGB/RGB-D images) for graph matching, which extends the concept of“unsupervised learning for graph matching.” Furthermore, this study can be also regarded as the first known approach to formulating “maximal graph mining” in the graph domain of ARGs. Our method exhibits superior performance on RGB and RGB-D images. The code will be published later.