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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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Submitted on : Thursday, March 19, 2015 - 1:24:04 PM
Last modification on : Monday, July 22, 2019 - 11:00:09 AM
Document(s) archivé(s) le : Monday, April 17, 2017 - 6:38:20 PM


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  • HAL Id : hal-01133447, version 1


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. ⟨hal-01133447⟩



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