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Learning structured models using maximum margin techniques has become an indispensable tool for com- puter vision researchers, as many computer vision applications can be cast naturally as an image labeling problem. Pixel-based or superpixel-based conditional random fields are particularly popular examples. Typ- ically, neighborhood graphs, which contain a large number of cycles, are used. As exact inference in loopy graphs is NP-hard in general, learning these models without approximations isarXiv:1309.4061v1 fatcat:olaovxp4ofcvbazvkikdjyrooi