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Conference Papers Year : 2012

Graph Matching via Sequential Monte Carlo

Yumin Suh
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Kyoung Mu Lee
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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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Dates and versions

hal-01064703 , version 1 (17-09-2014)


  • HAL Id : hal-01064703 , version 1


Yumin Suh, Minsu Cho, Kyoung Mu Lee. Graph Matching via Sequential Monte Carlo. ECCV - European Conference on Computer Vision, Sep 2012, Firenze, Italy. ⟨hal-01064703⟩
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