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As applied in the field of [[computer vision]], '''[[graph cut optimization]]''' can be employed to [[Polynomial time|efficiently]] solve a wide variety of low-level computer vision problems (''early vision''<ref>Adelson, Edward H., and James R. Bergen (1991), "[http://persci.mit.edu/pub_pdfs/elements91.pdf The plenoptic function and the elements of early vision]", Computational models of visual processing 1.2 (1991).</ref>), such as [[image smoothing]], the stereo [[correspondence problem]], [[image segmentation]], [[object co-segmentation]], and many other computer vision problems that can be formulated in terms of [[energy minimization]].
Many of these energy minimization problems can be approximated by solving a [[maximum flow problem]] in a [[Graph (discrete mathematics)|graph]]<ref>Boykov, Y., Veksler, O., and Zabih, R. (2001), "[
Under most formulations of such problems in computer vision, the minimum energy solution corresponds to the [[Maximum a posteriori estimation|maximum a posteriori estimate]] of a solution.
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The foundational theory of [[Cut (graph theory)|graph cuts]] was first applied in [[computer vision]] in the seminal paper by Greig, Porteous and Seheult<ref name="D.M. Greig, B.T 1989">D.M. Greig, B.T. Porteous and A.H. Seheult (1989), ''[https://classes.cs.uchicago.edu/archive/2006/fall/35040-1/gps.pdf Exact maximum a posteriori estimation for binary images]'', Journal of the Royal Statistical Society, Series B, '''51''', 271–279.</ref> of [[Durham University]]. Allan Seheult and Bruce Porteous were members of Durham's lauded statistics group of the time, led by [[Julian Besag]] and [[Peter Green (statistician)|Peter Green]], with the optimisation expert [[Margaret Greig]] notable as the first ever female member of staff of the Durham Mathematical Sciences Department.
In the [[Bayesian statistics|Bayesian]] statistical context of smoothing noisy (or corrupted) images, they showed how the [[MAP estimate|maximum a posteriori estimate]] of a [[binary image]] can be obtained ''exactly'' by maximizing the [[Flow network|flow]] through an associated image network, involving the introduction of a ''source'' and ''sink''. The problem was therefore shown to be efficiently solvable. Prior to this result, ''approximate'' techniques such as [[simulated annealing]] (as proposed by the [[Donald Geman|Geman brothers]]),<ref>D. Geman and S. Geman (1984), ''[
Although the general [[Graph coloring|<math>k</math>-colour problem]] is NP hard for <math>k > 2,</math> the approach of Greig, Porteous and Seheult<ref name="D.M. Greig, B.T 1989" /> has turned out<ref>Y. Boykov, O. Veksler and R. Zabih (1998), "[
In 2011, C. Couprie ''et al''.<ref>Camille Couprie, Leo Grady, Laurent Najman and Hugues Talbot, "[http://leogrady.net/wp-content/uploads/2017/01/couprie2011power.pdf Power Watersheds: A Unifying Graph-Based Optimization Framework]”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 7, pp. 1384-1399, July 2011</ref> proposed a general image segmentation framework, called the "Power Watershed", that minimized a real-valued [[indicator function]] from [0,1] over a graph, constrained by user seeds (or unary terms) set to 0 or 1, in which the minimization of the indicator function over the graph is optimized with respect to an exponent <math>p</math>. When <math>p=1</math>, the Power Watershed is optimized by graph cuts, when <math>p=0</math> the Power Watershed is optimized by shortest paths, <math>p=2</math> is optimized by the [[random walker algorithm]] and <math>p=\infty</math> is optimized by the [[Watershed (image processing)|watershed]] algorithm. In this way, the Power Watershed may be viewed as a generalization of graph cuts that provides a straightforward connection with other energy optimization segmentation/clustering algorithms.
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