Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost

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 multidimen...

Full description

Saved in:
Bibliographic Details
Published inJournal of mathematical imaging and vision Vol. 59; no. 2; pp. 161 - 186
Main Authors Papadakis, Nicolas, Rabin, Julien
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2017
Springer Nature B.V
Springer Verlag
Subjects
Applications of Mathematics
Computer Science
Histograms
Image Processing and Computer Vision
Image segmentation
Mathematical Methods in Physics
Signal and Image Processing
Signal,Image and Speech Processing
Statistical distributions
Transport theory
Image segmentation
Wasserstein distance
Convex optimization
Optimal transport
Sinkhorn distance
Online AccessGet full text

Cover

More Information
Summary: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 reference 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 multiphase segmentation as well as co-segmentation of multiple images.
ISSN:0924-9907
1573-7683
DOI:10.1007/s10851-017-0725-5