Generalized Roof Duality for Pseudo-Boolean Optimization
(2011) IEEE International Conference on Computer Vision (ICCV), 2011 p.255-262- Abstract
- The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized.
We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as... (More) - The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized.
We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as persistency still hold and how the relaxations can be efficiently constructed for general cubic and quartic pseudo-boolean functions. From a practical point of view, we show that our relaxations perform better than state-of-the-art for a wide range of problems, both in terms of lower bounds and in the number of assigned variables. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2225996
- author
- Kahl, Fredrik LU and Strandmark, Petter LU
- organization
- publishing date
- 2011
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE International Conference on Computer Vision (ICCV)
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Computer Vision (ICCV), 2011
- conference location
- Barcelona, Spain
- conference dates
- 2011-11-06 - 2011-11-13
- external identifiers
-
- wos:000300061900033
- scopus:84856686367
- DOI
- 10.1109/ICCV.2011.6126250
- language
- English
- LU publication?
- yes
- id
- edc225e6-2f9b-41f9-b0c9-630389f1491b (old id 2225996)
- date added to LUP
- 2016-04-04 10:51:23
- date last changed
- 2022-01-29 20:58:30
@inproceedings{edc225e6-2f9b-41f9-b0c9-630389f1491b, abstract = {{The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized. <br/><br> We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as persistency still hold and how the relaxations can be efficiently constructed for general cubic and quartic pseudo-boolean functions. From a practical point of view, we show that our relaxations perform better than state-of-the-art for a wide range of problems, both in terms of lower bounds and in the number of assigned variables.}}, author = {{Kahl, Fredrik and Strandmark, Petter}}, booktitle = {{IEEE International Conference on Computer Vision (ICCV)}}, language = {{eng}}, pages = {{255--262}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Generalized Roof Duality for Pseudo-Boolean Optimization}}, url = {{http://dx.doi.org/10.1109/ICCV.2011.6126250}}, doi = {{10.1109/ICCV.2011.6126250}}, year = {{2011}}, }