  We investigate in this work a versatile convex framework for multiple image
segmentation, relying on the regularized optimal mass transport theory. In this
setting, several transport cost functions are considered and used to match
statistical distributions of features. In practice, global multidimensional
histograms are estimated from the segmented image regions, and are compared to
referring models that are either fixed histograms given a priori, or directly
inferred in the non-supervised case. The different convex problems studied are
solved efficiently using primal-dual algorithms. The proposed approach is
generic and enables multi-phase segmentation as well as co-segmentation of
multiple images.
