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SCALESPACE
2015
Springer

Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts

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Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts
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.
Jörg Hendrik Kappes, Paul Swoboda, Bogdan Sav
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SCALESPACE
Authors Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr
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