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

• Published in: IEEE transactions on pattern analysis and machine intelligence 2008-06, Vol.30 (6), p.1068-1080
• Main Authors: Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.
• Format: Article
• Language: English
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial Intelligence; Belief propagation; Benchmarks; Computer science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Computer vision; Educational institutions; Energy conservation; Energy use; Exact sciences and technology; Global optimization; Graph cuts; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Markov Chains; Markov random fields; Minimization; Minimization methods; Models, Statistical; Noise reduction; Optimization; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Performance evaluation of algorithms and systems; Reproducibility of Results; Runtime; Sensitivity and Specificity; Software; Studies; Switches; Texture; Theoretical computing
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2007.70844

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/.