Image Segmentation by Branch-and-Mincut

12 years 7 months ago
Image Segmentation by Branch-and-Mincut
Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from lowlevel cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branchand-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, an...
Victor S. Lempitsky, Andrew Blake, Carsten Rother
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2008
Where ECCV
Authors Victor S. Lempitsky, Andrew Blake, Carsten Rother
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