NDLI: A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

Content Provider	IEEE Xplore Digital Library
Author	Szeliski, R. Zabih, R. Scharstein, D. Veksler, O. Kolmogorov, V. Agarwala, A. Tappen, M. Rother, C.
Copyright Year	1979
Abstract	Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: For example, such methods form the basis for almost all the top-performing stereo methods. However, the trade-offs among different energy minimization algorithms are still not well understood. In this paper, we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms. We investigate three promising methods-graph cuts, LBP, and tree-reweighted message passing-in addition to the well-known older iterated conditional mode (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. The benchmarks, code, images, and results are available at http://vision.middlebury.edu/MRF/.
Sponsorship	IEEE Computer Society
Page Count	13
File Size	3741509
Starting Page	1068
Ending Page	1080
File Format	PDF
ISSN	01628828
Volume Number	30
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Minimization methods Markov random fields Belief propagation Computer science Educational institutions Image segmentation Switches Computer vision Runtime Noise reduction Performance evaluation of algorithms and systems Global optimization Graph cuts
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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