Convex Color Image Segmentation with Optimal Transport Distances

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.

A short version of this report has been submitted to the Fifth International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2015

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