Title

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

Authors

Authors

R. Szeliski; R. Zabih; D. Scharstein; O. Veksler; V. Kolmogorov; A. Agarwala; M. Tappen;C. Rother

Comments

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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

WOS:000254872500011

ISSN

0162-8828

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