Semi-supervised segmentation based on non-local continuous min-cut

Nawal Houhou*, Xavier Bresson, Arthur Szlam, Tony F. Chan, Jean Philippe Thiran

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations


We propose a semi-supervised image segmentation method that relies on a non-local continuous version of the min-cut algorithm and labels or seeds provided by a user. The segmentation process is performed via energy minimization. The proposed energy is composed of three terms. The first term defines labels or seed points assigned to objects that the user wants to identify and the background. The second term carries out the diffusion of object and background labels and stops the diffusion when the interface between the object and the background is reached. The diffusion process is performed on a graph defined from image intensity patches. The graph of intensity patches is known to better deal with textures because this graph uses semi-local and non-local image information. The last term is the standard TV term that regularizes the geometry of the interface. We introduce an iterative scheme that provides a unique minimizer. Promising results are presented on synthetic textures a nd real-world images.

Original languageEnglish (US)
Title of host publicationScale Space and Variational Methods in Computer Vision - Second International Conference, SSVM 2009, Proceedings
Number of pages12
StatePublished - 2009
Externally publishedYes
Event2nd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2009 - Voss, Norway
Duration: Jun 1 2009Jun 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5567 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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