Convex Color Image Segmentation with Optimal Transport Distances

Julien Rabin 1 Nicolas Papadakis 2
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation. In the considered framework, fixed exemplar histograms define a prior on the statistical features of the two regions in competition. In this paper, we investigate the use of various transport-based cost functions as discrepancy measures and rely on a primal-dual algorithm to solve the obtained convex optimization problem.
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Communication dans un congrès
Scale Space and Variational Methods in Computer Vision, May 2015, Lège Cap Ferret, France. Proceedings of the fifth International Conference on Scale Space and Variational Methods in Computer Vision, 2015, Image Processing, Computer Vision, Pattern Recognition, and Graphics
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Contributeur : Julien Rabin <>
Soumis le : jeudi 19 mars 2015 - 13:24:04
Dernière modification le : jeudi 28 mai 2015 - 01:09:25

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Julien Rabin, Nicolas Papadakis. Convex Color Image Segmentation with Optimal Transport Distances. Scale Space and Variational Methods in Computer Vision, May 2015, Lège Cap Ferret, France. Proceedings of the fifth International Conference on Scale Space and Variational Methods in Computer Vision, 2015, Image Processing, Computer Vision, Pattern Recognition, and Graphics. <hal-01133447>

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