A co-occurrence prior for continuous multi-label optimization

Souiai M, Strekalovskiy E, Nieuwenhuis C, Cremers D (2013)

Publication Type: Conference contribution

Publication year: 2013

Journal

Lecture Notes in Computer Science Springer Verlag

Book Volume: 8081 LNCS

Pages Range: 209-222

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: SWE

ISBN: 9783642403941

DOI: 10.1007/978-3-642-40395-8_16

Abstract

To obtain high-quality segmentation results the integration of semantic information is indispensable. In contrast to existing segmentation methods which use a spatial regularizer, i.e. a local interaction between image points, the co-occurrence prior [15] imposes penalties on the co-existence of different labels in a segmentation. We propose a continuous domain formulation of this prior, using a convex relaxation multi-labeling approach. While the discrete approach [15] is employs minimization by sequential alpha expansions, our continuous convex formulation is solved by efficient primal-dual algorithms, which are highly parallelizable on the GPU. Also, our framework allows isotropic regularizers which do not exhibit grid bias. Experimental results on the MSRC benchmark confirm that the use of co-occurrence priors leads to drastic improvements in segmentation compared to the classical Potts model formulation when applied. © 2013 Springer-Verlag.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Souiai, M., Strekalovskiy, E., Nieuwenhuis, C., & Cremers, D. (2013). A co-occurrence prior for continuous multi-label optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 209-222). SWE.

MLA:

Souiai, Mohamed, et al. "A co-occurrence prior for continuous multi-label optimization." Proceedings of the 9th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2013, SWE 2013. 209-222.

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