Convex optimization for scene understanding
Souiai M, Nieuwenhuis C, Strekalovskiy E, Cremers D (2013)
Publication Type: Conference contribution
Publication year: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 9-14
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: AUS
ISBN: 9781479930227
Abstract
In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or super pixels. The central idea is to integrate a hierarchical label prior and a set of convex constraints into the segmentation approach, which combine the three tasks by introducing high-level scene information. Instead of learning label co-occurrences from limited benchmark training data, the hierarchical prior comes naturally with the way humans see their surroundings. © 2013 IEEE.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
University of California, Berkeley
United States (USA) (US)
Germany (DE)
University of California, Berkeley
United States (USA) (US)
How to cite
APA:
Souiai, M., Nieuwenhuis, C., Strekalovskiy, E., & Cremers, D. (2013). Convex optimization for scene understanding. In Proceedings of the IEEE International Conference on Computer Vision (pp. 9-14). AUS: Institute of Electrical and Electronics Engineers Inc..
MLA:
Souiai, Mohamed, et al. "Convex optimization for scene understanding." Proceedings of the 2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013, AUS Institute of Electrical and Electronics Engineers Inc., 2013. 9-14.
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