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...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Victor S. Lempitsky, Andrew Blake, Carsten Rother
• Keywords: Binary Image Segmentation, Category-specific Shape Segmentation, Computer Vision, ECCV 2008, Segmentation Process, Segmentation Scenarios, Unsupervised Segmentation