@article {Lempitsky2012,
	title = {Branch-and-mincut: Global optimization for image segmentation with high-level priors},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {44},
	number = {3},
	year = {2012},
	pages = {315{\textendash}329},
	abstract = {Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level 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, and the segmentation under intensity/color priors defined by Chan-Vese and GrabCut functionals. {\textcopyright} Springer Science+Business Media, LLC 2011.},
	keywords = {Branch-and-bound, Global optimization, Graph Cuts, Image segmentation, Shape priors},
	issn = {09249907},
	doi = {10.1007/s10851-012-0328-0},
	author = {Lempitsky, Victor and Blake, Andrew and Carsten Rother}
}
