NDLI: Fast Energy Minimization Using Learned State Filters

Content Provider	IEEE Xplore Digital Library
Author	Guillaumin, M. Van Gool, L. Ferrari, V.
Copyright Year	2013
Abstract	Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. These factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. The parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions.
Starting Page	1682
Ending Page	1689
File Size	520391
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.220
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computational modeling Minimization Semantics Inference algorithms Labeling Approximation algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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