Learning CRFs using graph cuts

Martin Szummer, Pushmeet Kohli, Derek Hoiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an intractable problem. This paper shows how to apply fast inference algorithms, in particular graph cuts, to learn parameters of random fields with similar efficiency. We find optimal parameter values under standard regularized objective functions that ensure good generalization. Our algorithm enables learning of many parameters in reasonable time, and we explore further speedup techniques. We also discuss extensions to non-associative and multi-class problems. We evaluate the method on image segmentation and geometry recognition.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
Number of pages14
EditionPART 2
ISBN (Print)3540886850, 9783540886853
StatePublished - 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5303 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th European Conference on Computer Vision, ECCV 2008

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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