Title: Probabilistic Hypergraph Matching Based on Affinity Tensor Updating
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| Authors: Yang, X; Liu, ZY; Qiao, H; Su, JH
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| Author Full Names: Yang, Xu; Liu, Zhi-Yong; Qiao, Hong; Su, Jian-Hua
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| Source: NEUROCOMPUTING, 269 142-147; SI 10.1016/j.neucom.2016.12.096 DEC 20 2017
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| Language: English
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| Abstract: Graph matching is a fundamental problem in artificial intelligence and structural data processing. Hypergraph matching has recently become popular in the graph matching community. Existing hypergraph matching algorithms usually resort to the continuous methods, while the combinatorial nature of hypergraph matching is not well considered. Therefore in this paper, we propose a novel hypergraph matching algorithm by introducing the affinity tensor updating based graduated projection. Specifically, the hypergraph matching problem is first formulated as a combinatorial optimization problem in a high order polynomial form. Then this NP-hard problem is relaxed and interpreted in a probabilistic manner, which is approximately solved by iterative techniques. The updating of the affinity tensor is performed in each iteration, besides the updating of probabilistic assignment vector. Experimental results on both synthetic and real-world datasets witness the effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.
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| ISSN: 0925-2312
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| eISSN: 1872-8286
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| IDS Number: FI8QA
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| Unique ID: WOS:000412266000017
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