Abstract:
We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Com...Show MoreMetadata
Abstract:
We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Compatibility Estimation (MCCE), which transforms the compatibility tensor representing hyper-edge similarities into a compatibility matrix representing edge similarities. We then cluster graph vertices associated with the compatibility matrix and extract its dominant set as the optimal matches. Our MCCE-based method overcomes the information loss arising in arithmetic average, which is commonly used for marginal-ization in the hypergraph matching literature. Experiments demonstrate the effectiveness of our method.
Date of Conference: 11-15 November 2012
Date Added to IEEE Xplore: 14 February 2013
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Conference Location: Tsukuba, Japan
University of Electronic Science and Technology of China, China
University of Electronic Science and Technology of China, China
China University of Petroleum Huadong, China
University of York, UK
Contents University of Electronic Science and Technology of China, China
University of Electronic Science and Technology of China, China
China University of Petroleum Huadong, China
University of York, UK
