A comparative study of energy minimization methods for Markov random fields

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A comparative study of energy minimization methods for Markov random fields (2006)

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by Richard Szeliski , Ramin Zabih , Daniel Scharstein , Olga Veksler , Aseem Agarwala , Carsten Rother, et al.

Venue:	IN ECCV
Citations:	415 - 36 self

BibTeX

@INPROCEEDINGS{Szeliski06acomparative,
    author = {Richard Szeliski and Ramin Zabih and Daniel Scharstein and Olga Veksler and Aseem Agarwala and Carsten Rother and et al.},
    title = {A comparative study of energy minimization methods for Markov random fields},
    booktitle = {IN ECCV},
    year = {2006},
    pages = {16--29},
    publisher = {}
}

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Abstract

One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Random Fields (MRF’s), the resulting energy minimization problems were 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. Unfortunately, most papers define their own energy function, which is minimized with a specific algorithm of their choice. As a result, the tradeoffs among different energy minimization algorithms are not well understood. In this paper we describe a set of energy minimization benchmarks, which we use to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods—graph cuts, LBP, and tree-reweighted message passing—as well as the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching and interactive segmentation. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead. We expect that the availability of our benchmarks and interface will make it significantly easier for vision researchers to adopt the best method for their specific problems. Benchmarks, code, results and images are available at

Keyphrases

markov random field comparative study energy minimization method vision researcher energy function conditional mode early vision energy minimization benchmark efficient energy minimization algorithm top-performing stereo method loopy belief propagation many problem different energy minimization algorithm interactive segmentation benchmark problem general-purpose software interface minimal overhead graph cut running time solution quality exciting advance well-known older optimization method energy minimization problem several common energy minimization algorithm many early vision task specific algorithm recent method graph cut image stitching specific problem tree-reweighted message passing

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