In 2011, C. Couprie et al. 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 formula_3. When formula_4, the Power Watershed is optimized by graph cuts, when formula_5 the Power Watershed is optimized by shortest paths, formula_6 is optimized by the Random walker algorithm and formula_7 is optimized by the Watershed (image processing) 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.