Multicuts and Perturb & MAP for Probabilistic Graph Clustering

9 Jan 2016Jörg Hendrik KappesPaul SwobodaBogdan SavchynskyyTamir HazanChristoph Schnörr

We present a probabilistic graphical model formulation for the graph clustering problem. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions... (read more)

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