Article

Graph matching via sequential monte carlo

Authors:

Kyoung Mu LeeAuthors Info & Claims

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

Pages 624 - 637

https://doi.org/10.1007/978-3-642-33712-3_45

Published: 07 October 2012 Publication History

Abstract

Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers.

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Cited By

Yu TYan JLiu WLi B(2018)Incremental Multi-graph Matching via Diversity and Randomness Based Graph ClusteringComputer Vision – ECCV 201810.1007/978-3-030-01261-8_9(142-158)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01261-8_9
Wang RLiang DZhang WCao XHanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)MatchDRProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967293(606-610)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1145/2964284.2967293
Yan JYin XLin WDeng CZha HYang XKender JSmith JLuo JBoll SHsu W(2016)A Short Survey of Recent Advances in Graph MatchingProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912035(167-174)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912035

Graph matching via sequential monte carlo
1. Computing methodologies

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

October 2012

880 pages

ISBN:9783642337116

Editors:
Andrew Fitzgibbon
Microsoft Research Ltd., Cambridge, UK
,
Svetlana Lazebnik
Dept. of Computer Science, University of North Carolina, Chapel Hill, NC, UK
,
Pietro Perona
Dept. of Computer Science, California Institute of Technology, Pasadena, CA, UK
,
Yoichi Sato
Institute of Industrial Science, The University of Tokyo, Pasadena, Tokyo, Japan
,
Cordelia Schmid
Institute of Industrial Science, INRIA, Montbonnot, Tokyo, France

Sponsors

TOYOTA: TOYOTA
Google Inc.
IBMR: IBM Research
NVIDIA
Microsoft Reasearch: Microsoft Reasearch

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 October 2012

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Cited By

Yu TYan JLiu WLi B(2018)Incremental Multi-graph Matching via Diversity and Randomness Based Graph ClusteringComputer Vision – ECCV 201810.1007/978-3-030-01261-8_9(142-158)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01261-8_9
Wang RLiang DZhang WCao XHanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)MatchDRProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967293(606-610)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1145/2964284.2967293
Yan JYin XLin WDeng CZha HYang XKender JSmith JLuo JBoll SHsu W(2016)A Short Survey of Recent Advances in Graph MatchingProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912035(167-174)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912035

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