A novel stochastic attributed relational graph matching based on relation vector space analysis

Authors:
Bo Gun Park;Kyoung Mu Lee;Sang Uk Lee
Affiliations:
School of Electrical Eng., ASRI, Seoul National University, Seoul, Korea;School of Electrical Eng., ASRI, Seoul National University, Seoul, Korea;School of Electrical Eng., ASRI, Seoul National University, Seoul, Korea
Venue:
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Year:
2006

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Abstract

In this paper, we propose a novel stochastic attributed relational graph (SARG) matching algorithm in order to cope with possible distortions due to noise and occlusion. The support flow and the correspondence measure between nodes are defined and estimated by analyzing the distribution of the attribute vectors in the relation vector space. And then the candidate subgraphs are extracted and ordered according to the correspondence measure. Missing nodes for each candidates are identified by the iterative voting scheme through an error analysis, and then the final subgraph matching is carried out effectively by excluding them. Experimental results on the synthetic ARGs demonstrate that the proposed SARG matching algorithm is quite robust and efficient even in the noisy environment. Comparative evaluation results also show that it gives superior performance compared to other conventional graph matching approaches.