@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}
}
@conference {Rother2004,
	title = {"GrabCut" - Interactive foreground extraction using iterated graph cuts},
	booktitle = {ACM Transactions on Graphics},
	volume = {23},
	number = {3},
	year = {2004},
	pages = {309{\textendash}314},
	abstract = {The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.},
	keywords = {Alpha Matting, Foreground extraction, Graph Cuts, Image Editing, Interactive Image Segmentation},
	issn = {07300301},
	doi = {10.1145/1015706.1015720},
	author = {Carsten Rother and Kolmogorov, Vladimir and Blake, Andrew}
}
