Higher-order gradient descent by fusion-move graph cut

Hiroshi Ishikawa
2009 2009 IEEE 12th International Conference on Computer Vision  
Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higherorder 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
more » ... r 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.
doi:10.1109/iccv.2009.5459187 dblp:conf/iccv/Ishikawa09 fatcat:cq6pd6ttqvaxzaoo2gi7vvkijm