NDLI: A tiered move-making algorithm for general pairwise MRFs

Content Provider	IEEE Xplore Digital Library
Author	Vineet, V. Warrell, J. Torr, P.H.S.
Copyright Year	2012
Description	Author affiliation: Oxford Brookes University (Vineet, V.; Warrell, J.; Torr, P.H.S.)
Abstract	A large number of problems in computer vision can be modeled as energy minimization problems in a markov random field (MRF) framework. Many methods have been developed over the years for efficient inference, especially in pairwise MRFs. In general there is a trade-off between the complexity/efficiency of the algorithm and its convergence properties, with certain problems requiring more complex inference to handle general pairwise potentials. Graphcuts based α-expansion performs well on certain classes of energies, and sequential tree reweighted message passing (TRWS) and loopy belief propagation (LBP) can be used for non-submodular cases. These methods though suffer from poor convergence and often oscillate between solutions. In this paper, we propose a tiered move making algorithm which is an iterative method. Each move to the next configuration is based on the current labeling and an optimal tiered move, where each tiered move requires one application of the dynamic programming based tiered labeling method introduced in Felzenszwalb et. al. [2]. The algorithm converges to a local minimum for any general pairwise potential, and we give a theoretical analysis of the properties of the algorithm, characterizing the situations in which we can expect good performance. We evaluate the algorithm on many benchmark labeling problems such as stereo, image segmentation, image stitching and image denoising, as well as random energy minimization. Our method consistently gets better energy values than α-expansion, LBP, quadratic pseudo-boolean optimization (QPBO), and is competitive with TRWS.
Starting Page	1632
Ending Page	1639
File Size	517472
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247856
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Algorithm design and analysis Complexity theory Heuristic algorithms Radio frequency Belief propagation Stereo vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A tiered move-making algorithm for general pairwise MRFs. In: Computer vision and pattern recognition conference (2012)

A tiered move-making algorithm for general pairwise mrfs.

Reduce, reuse & recycle: Efficiently solving multi-label MRFs

Learning and Inference for General Non Submodular Pairwise MRFs

Bilayer Stereo Matching

A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies

$(BP)^{2}:$ Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies

A constant-space belief propagation algorithm for stereo matching

SPM-BP: Sped-Up PatchMatch Belief Propagation for Continuous MRFs

A tiered move-making algorithm for general pairwise MRFs

Similar Documents

A tiered move-making algorithm for general pairwise MRFs. In: Computer vision and pattern recognition conference (2012)

A tiered move-making algorithm for general pairwise mrfs.

Reduce, reuse & recycle: Efficiently solving multi-label MRFs

Learning and Inference for General Non Submodular Pairwise MRFs

Bilayer Stereo Matching

A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies

$(BP)^{2}:$ Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies

A constant-space belief propagation algorithm for stereo matching

SPM-BP: Sped-Up PatchMatch Belief Propagation for Continuous MRFs

A tiered move-making algorithm for general pairwise MRFs