Multi-label Moves for MRFs with Truncated Convex Priors

Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize. One approach to approximate optimization is the so-called move making algorithms. At each iteration, a move-making algorithm ma...

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Bibliographic Details
Published in:International journal of computer vision Vol. 98; no. 1; pp. 1 - 14
Main Author: Veksler, Olga
Format: Journal Article
Language:English
Published: Boston Springer US 01.05.2012
Springer
Springer Nature B.V
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Analysis
Applied sciences
Approximation
Artificial Intelligence
Boolean
Computer Imaging
Computer Science
Computer science; control theory; systems
Computer vision
Energy
Exact sciences and technology
Graph theory
Graphs
Image Processing and Computer Vision
Information retrieval. Graph
Labels
Magnetorheological fluids
Markov analysis
Mathematical optimization
Optimization
Optimization techniques
Pattern Recognition
Pattern Recognition and Graphics
Pattern recognition. Digital image processing. Computational geometry
Pixels
Studies
Switches
Theoretical computing
Vision
Vision systems
Truncated convex priors
Discrete optimization
Markov random fields (MRF)
Graph cuts
Markov process
Probabilistic approach
Image processing
Energy function
Graph theory
Image restoration
Optimization
Graph cut
NP hard problem
Truncated shape
Discrete programming
Adulteration
ISSN:0920-5691, 1573-1405
Online Access:Get full text
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Summary:Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize. One approach to approximate optimization is the so-called move making algorithms. At each iteration, a move-making algorithm makes a proposal (move) for a pixel p to either keep its old label or switch to a new label. Two move-making algorithms based on graph cuts are in wide use, namely the swap and expansion. Both of these moves are binary in nature, that is they give each pixel a choice of only two labels. An evaluation of optimization techniques shows that the expansion and swap algorithms perform very well for energies where the underlying MRF has the Potts prior. However for more general priors, the swap and expansion algorithms do not perform as well. The main contribution of this paper is to develop multi-label moves. A multi-label move, unlike expansion and swap, gives each pixel has a choice of more than two labels to switch to. In particular, we develop several multi-label moves for truncated convex priors. We evaluate our moves on image restoration, inpainting, and stereo correspondence. We get better results than expansion and swap algorithms, both in terms of the energy value and accuracy.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-011-0491-6