Probabilistic correlation clustering and image partitioning using perturbed Multicuts

TitleProbabilistic correlation clustering and image partitioning using perturbed Multicuts
Publication TypeConference Paper
Year of Publication2015
AuthorsKappes, JHendrik, Swoboda, P, Savchynskyy, B, Hazan, T, Schnörr, C
Conference NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN Number9783319184609
KeywordsCorrelation clustering, Multicut, Perturb and MAP
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.

DOI10.1007/978-3-319-18461-6_19
Citation KeyKappes2015c