Parallel and Distributed Graph Cuts by Dual Decomposition
(2010) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 p.2085-2092- Abstract
- Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables.
We demonstrate that our approach both allows
(i) faster processing on multi-core computers and
(ii) the capability to handle larger problems by splitting the graph across multiple... (More) - Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables.
We demonstrate that our approach both allows
(i) faster processing on multi-core computers and
(ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network.
Even though our approach does not give a theoretical guarantee of speed-up, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source C++ implementation of the dual decomposition method is also made publicly available. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1567612
- author
- Strandmark, Petter LU and Kahl, Fredrik LU
- organization
- publishing date
- 2010
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- mpi, supercomputer, parallel, graph cuts
- host publication
- IEEE Conference on Computer Vision and Pattern Recognition
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010
- conference location
- San Francisco, United States
- conference dates
- 2010-06-13 - 2010-06-18
- external identifiers
-
- wos:000287417502018
- scopus:77956008810
- ISSN
- 1063-6919
- ISBN
- 978-1-4244-6984-0
- DOI
- 10.1109/CVPR.2010.5539886
- language
- English
- LU publication?
- yes
- id
- 8d6fa8fb-cf7b-4e3f-b1f7-885f9e1f7c94 (old id 1567612)
- alternative location
- http://www.maths.lth.se/vision/publdb/reports/pdf/strandmark-kahl-cvpr-2010.pdf
- date added to LUP
- 2016-04-01 14:27:38
- date last changed
- 2022-04-30 01:56:55
@inproceedings{8d6fa8fb-cf7b-4e3f-b1f7-885f9e1f7c94,
abstract = {{Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables.<br/><br>
<br/><br>
We demonstrate that our approach both allows <br/><br>
(i) faster processing on multi-core computers and <br/><br>
(ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network. <br/><br>
Even though our approach does not give a theoretical guarantee of speed-up, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source C++ implementation of the dual decomposition method is also made publicly available.}},
author = {{Strandmark, Petter and Kahl, Fredrik}},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition}},
isbn = {{978-1-4244-6984-0}},
issn = {{1063-6919}},
keywords = {{mpi; supercomputer; parallel; graph cuts}},
language = {{eng}},
pages = {{2085--2092}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Parallel and Distributed Graph Cuts by Dual Decomposition}},
url = {{https://lup.lub.lu.se/search/files/3989365/1580403.pdf}},
doi = {{10.1109/CVPR.2010.5539886}},
year = {{2010}},
}