Abstract

In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in com- puter vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet because these graph constructions are complex and highly specific to a particular en- ergy function, graph cuts have seen limited application to date. In this paper we characterize the energy functions that can be minimized by graph cuts. Our results are restricted to energy functions with binary variables. However, our work generalizes many previous constructions, and is easily applicable to vision problems that involve large numbers of labels, such as stereo, motion, image restoration and scene reconstruc- tion. We present three main results: a necessary condition for any energy function that can be minimized by graph cuts; a sudficient condition for energy functions that can be written as a sum of functions of up to three variables at a time; and a general-purpose construction to minimize such an energy function. Researchers who are considering the use of graph cuts to optimize a particular energy function can use our results to de- termine if this is possible, and then follow our construction to create the appropriate graph.