MRF optimization with separable convex prior on partially ordered labels
Domokos C, Schmidt FR, Cremers D (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11212 LNCS
Pages Range: 341-356
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783030012366
DOI: 10.1007/978-3-030-01237-3_21
Abstract
Solving a multi-labeling problem with a convex penalty can be achieved in polynomial time if the label set is totally ordered. In this paper we propose a generalization to partially ordered sets. To this end, we assume that the label set is the Cartesian product of totally ordered sets and the convex prior is separable. For this setting we introduce a general combinatorial optimization framework that provides an approximate solution. More specifically, we first construct a graph whose minimal cut provides a lower bound to our energy. The result of this relaxation is then used to get a feasible solution via classical move-making cuts. To speed up the optimization, we propose an efficient coarse-to-fine approach over the label space. We demonstrate the proposed framework through extensive experiments for optical flow estimation.
Involved external institutions
Germany (DE)How to cite
APA:
Domokos, C., Schmidt, F.R., & Cremers, D. (2018). MRF optimization with separable convex prior on partially ordered labels. In Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 341-356). Munich, DEU: Springer Verlag.
MLA:
Domokos, Csaba, Frank R. Schmidt, and Daniel Cremers. "MRF optimization with separable convex prior on partially ordered labels." Proceedings of the 15th European Conference on Computer Vision, ECCV 2018, Munich, DEU Ed. Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert, Springer Verlag, 2018. 341-356.
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