Title
A comparative study of energy minimization methods for Markov random fields with smoothness-based priors
Abbreviated Journal Title
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
performance evaluation; Markov random fields; global optimization; graph; cuts; belief propagation; GRAPH CUTS; IMAGE; VISION; SEGMENTATION; ALGORITHM; TEXTURES; STEREO; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic
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. Recently, 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 recent 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/.
Journal Title
Ieee Transactions on Pattern Analysis and Machine Intelligence
Volume
30
Issue/Number
6
Publication Date
1-1-2008
Document Type
Article
Language
English
First Page
1068
Last Page
1080
WOS Identifier
ISSN
0162-8828
Recommended Citation
"A comparative study of energy minimization methods for Markov random fields with smoothness-based priors" (2008). Faculty Bibliography 2000s. 1043.
https://stars.library.ucf.edu/facultybib2000/1043
Comments
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