A comparative study of energy minimization methods for Markov random fields with smoothness-based priors

R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, C. Rother, IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008) 1068–1080.

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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/.
Publishing Year
Date Published
2008-06-01
Journal Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
30
Issue
6
Page
1068 - 1080
IST-REx-ID

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Szeliski R, Zabih R, Scharstein D, et al. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2008;30(6):1068-1080. doi:10.1109/TPAMI.2007.70844
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., … Rother, C. (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 1068–1080. https://doi.org/10.1109/TPAMI.2007.70844
Szeliski, Richard, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother. “A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors.” IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 6 (2008): 1068–80. https://doi.org/10.1109/TPAMI.2007.70844.
R. Szeliski et al., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 6, pp. 1068–1080, 2008.
Szeliski R, Zabih R, Scharstein D, Veksler O, Kolmogorov V, Agarwala A, Tappen M, Rother C. 2008. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(6), 1068–1080.
Szeliski, Richard, et al. “A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 6, IEEE, 2008, pp. 1068–80, doi:10.1109/TPAMI.2007.70844.

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