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.
Originalsprache | Englisch |
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Titel | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Seitenumfang | 9 |
Herausgeber (Verlag) | IEEE |
Erscheinungsdatum | 01.06.2016 |
Seiten | 3948-3956 |
ISBN (Print) | 978-1-4673-8852-8 |
ISBN (elektronisch) | 978-1-4673-8851-1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 01.06.2016 |
Veranstaltung | 2016 IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, USA / Vereinigte Staaten Dauer: 27.06.2016 → 30.06.2016 |