Sublabel-Accurate Relaxation of Nonconvex Energies
Moellenhoff T, Laude E, Moeller M, Lellmann J, Cremers D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: IEEE Computer Society
Book Volume: 2016-December
Pages Range: 3948-3956
Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event location: Las Vegas, NV, USA
ISBN: 9781467388504
Abstract
We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels - see Fig. 1. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.
Involved external institutions
Germany (DE)How to cite
APA:
Moellenhoff, T., Laude, E., Moeller, M., Lellmann, J., & Cremers, D. (2016). Sublabel-Accurate Relaxation of Nonconvex Energies. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3948-3956). Las Vegas, NV, USA: IEEE Computer Society.
MLA:
Moellenhoff, Thomas, et al. "Sublabel-Accurate Relaxation of Nonconvex Energies." Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA IEEE Computer Society, 2016. 3948-3956.
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