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Conference papers

Graph Matching via Sequential Monte Carlo

Yumin Suh 1 Minsu Cho 2 Kyoung Mu Lee 1
2 WILLOW - Models of visual object recognition and scene understanding
CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique - ENS Paris
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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Submitted on : Wednesday, September 17, 2014 - 4:18:11 AM
Last modification on : Thursday, April 28, 2022 - 4:07:37 PM
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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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