Transformation of general binary mrf minimization to the first-order case

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

We introduce a transformation of general higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we formalize a framework for approximately minimizing higher-order multilabel MRF energies that combines the new reduction with the fusion-move and QPBO algorithms. While many computer vision problems today are formulated as energy minimization problems, they have mostly been limited to using first-order energies, which consist of unary and pairwise clique potentials, with a few exceptions that consider triples. This is because of the lack of efficient algorithms to optimize energies with higher-order interactions. Our algorithm challenges this restriction that limits the representational power of the models so that higher-order energies can be used to capture the rich statistics of natural scenes. We also show that some minimization methods can be considered special cases of the present framework, as well as comparing the new method experimentally with other such techniques.

Original languageEnglish
Article number5444874
Pages (from-to)1234-1249
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume33
Issue number6
DOIs
Publication statusPublished - 2011 Mar 15

Keywords

  • Energy minimization
  • graph cuts
  • higher-order MRFs
  • pseudo-Boolean function

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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