Higher-order segmentation via multicuts
Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetries. The basic Potts model and natural extensions thereof to higher-order models provide a prominent class of such object...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
[2016]
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| In: |
Computer vision and image understanding
Year: 2015, Jahrgang: 143, Pages: 104-119 |
| ISSN: | 1090-235X |
| DOI: | 10.1016/j.cviu.2015.11.005 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.cviu.2015.11.005 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S1077314215002490 
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| Verfasserangaben: | Jörg Hendrik Kappes, Markus Speth, Gerhard Reinelt, Christoph Schnörr |
| Zusammenfassung: | Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetries. The basic Potts model and natural extensions thereof to higher-order models provide a prominent class of such objectives, that cover a broad range of segmentation problems relevant to image analysis and computer vision. We exhibit a way to systematically take into account such higher-order terms for computational inference. Furthermore, we present results of a comprehensive and competitive numerical evaluation of a variety of dedicated cutting-plane algorithms. Our approach enables the globally optimal evaluation of a significant subset of these models, without compromising runtime. Polynomially solvable relaxations are studied as well, along with advanced rounding schemes for post-processing. |
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| Beschreibung: | Available online 21 November 2015 Gesehen am 07.05.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1090-235X |
| DOI: | 10.1016/j.cviu.2015.11.005 |