Higher-order gradient descent by fusion-move graph cut

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

22 Citations (Scopus)

Abstract

Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higher-order graph cuts: the combination of i) the fusion move algorithm, ii) the reduction of higher-order binary energy minimization to first-order, and iii) the QPBO algorithm. In the fusion move, it is crucial for the success and efficiency of the optimization to provide proposals that fits the energies being optimized. For higher-order energies, it is even more so because they have richer class of null potentials. In this paper, we focus on the efficiency of the higher-order graph cuts and present a simple technique for generating proposal labelings that makes the algorithm much more efficient, which we empirically show using examples in stereo and image denoising.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Pages568-574
Number of pages7
DOIs
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: 2009 Sep 292009 Oct 2

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference12th International Conference on Computer Vision, ICCV 2009
CountryJapan
CityKyoto
Period09/9/2909/10/2

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Higher-order gradient descent by fusion-move graph cut'. Together they form a unique fingerprint.

  • Cite this

    Ishikawa, H. (2009). Higher-order gradient descent by fusion-move graph cut. In 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009 (pp. 568-574). [5459187] (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2009.5459187