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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Quanshi Zhang Xuan Song Xiaowei Shao Huijing Zhao Shibasaki, R. |
| Copyright Year | 2014 |
| Description | Author affiliation: Univ. of Tokyo, Tokyo, Japan (Quanshi Zhang; Xuan Song; Xiaowei Shao; Shibasaki, R.) || Peking Univ., Beijing, China (Huijing Zhao) |
| Abstract | Graph matching and graph mining are two typical areas in artificial intelligence. In this paper, we define the soft attributed pattern (SAP) to describe the common subgraph 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 SAP with the maximal graph size without node enumeration. Given an initial graph template and a number of ARGs, we modify the graph template into the maximal SAP among the ARGs in an unsupervised fashion. The maximal SAP extraction 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. |
| Starting Page | 1394 |
| Ending Page | 1401 |
| File Size | 2493354 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479951185 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2014.181 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pattern matching Optimization Computer vision Data mining Computational modeling Minimization Educational institutions Attributed Relational Graphs Graph Mining Graph Matching |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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