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We study an extension of the classical graph cut problem, wherein we replacethe modular (sum of edge weights) cost function by a submodular set functiondefined over graph edges. Special cases of this problem have appeared indifferent applications in signal processing, machine learning, and computervision. In this paper, we connect these applications via the genericformulation of "cooperative graph cuts", for which we study complexity,algorithms, and connections to polymatroidal network flows. Finally, we comparethe proposed algorithms empirically.

Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms