@conference {Kappes2015c, title = {Probabilistic correlation clustering and image partitioning using perturbed Multicuts}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {9087}, year = {2015}, pages = {231{\textendash}242}, abstract = {We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. 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. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.}, keywords = {Correlation clustering, Multicut, Perturb and MAP}, isbn = {9783319184609}, issn = {16113349}, doi = {10.1007/978-3-319-18461-6_19}, author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr} }