Probabilistic correlation clustering and image partitioning using perturbed multicuts
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...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
28 April 2015
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| In: |
Scale Space and Variational Methods in Computer Vision
Year: 2015, Pages: 231-242 |
| DOI: | 10.1007/978-3-319-18461-6_19 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-319-18461-6_19 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-18461-6_19 
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| Verfasserangaben: | Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr |
MARC
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| 520 | |a 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. | ||
| 650 | 4 | |a Correlation clustering | |
| 650 | 4 | |a Multicut | |
| 650 | 4 | |a Perturb and MAP | |
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