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K. He, C. Rhemann, C. Rother, X. Tang, J. Sun:
"A Global Sampling Method for Alpha Matting";
Poster: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011, Colorado Springs; 06-21-2011 - 06-23-2011; in: "IEEE Computer Vision and Pattern Recognition", (2011), 8 pages.

Alpha matting refers to the problem of softly extracting
the foreground from an image. Given a trimap (specifying
known foreground/background and unknown pixels), a
straightforward way to compute the alpha value is to sample
some known foreground and background colors for each
unknown pixel. Existing sampling-based matting methods
often collect samples near the unknown pixels only. They
fail if good samples cannot be found nearby.
In this paper, we propose a global sampling method that
uses all samples available in the image. Our global sample
set avoids missing good samples. A simple but effective
cost function is defined to tackle the ambiguity in
the sample selection process. To handle the computational
complexity introduced by the large number of samples, we
pose the sampling task as a correspondence problem. The
correspondence search is efficiently achieved by generalizing
a randomized algorithm previously designed for patch
matching[3]. A variety of experiments show that our global
sampling method produces both visually and quantitatively
high-quality matting results.

http://publik.tuwien.ac.at/files/PubDat_207062.pdf