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GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs
Kangwei Liu1; Junge Zhang1; Peipei Yang1; Stephen Maybank2; Kaiqi Huang1
Source PublicationInternational Journal of Computer Vision
2017
Volume121Issue:3Pages:365-390
AbstractMarkov random fields (MRF) have become an
important tool for many vision applications, and the optimization of MRFs is a problem of fundamental importance.
Recently, Veksler and Kumar et al. proposed the range move
algorithms, which are some of the most successful optimizers. Instead of considering only two labels as in previous
move-making algorithms, they explore a large search space
over a range of labels in each iteration, and significantly outperform previous move-making algorithms. However, two
problemshavegreatlylimitedtheapplicabilityofrangemove
algorithms: (1) They are limited in the energy functions they
can handle (i.e., only truncated convex functions); (2) They
tend to be very slow compared to other move-making algorithms (e.g.,
α-expansion and αβ-swap). In this paper, we
propose two generalized range move algorithms (GRMA)
for the efficient optimization of MRFs. To address the first
problem,weextendtheGRMAstomoregeneralenergyfunctions by restricting the chosen labels in each move so that the
energy function is submodular on the chosen subset. Furthermore, we provide a feasible sufficient condition for choosing
these subsets of labels. To address the second problem, we
dynamically obtain the iterative moves by solving set cover
problems. This greatly reduces the number of moves during
the optimization. We also propose a fast graph construction
method for the GRMAs. Experiments show that the GRMAs 
offer a great speedup over previous range move algorithms,
while yielding competitive solutions.

KeywordMarkov Random Field Discrete Optimization Range Move Algorithms
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12423
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation1.CASIA
2.Birkbeck College, University of London
Recommended Citation
GB/T 7714
Kangwei Liu,Junge Zhang,Peipei Yang,et al. GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs[J]. International Journal of Computer Vision,2017,121(3):365-390.
APA Kangwei Liu,Junge Zhang,Peipei Yang,Stephen Maybank,&Kaiqi Huang.(2017).GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs.International Journal of Computer Vision,121(3),365-390.
MLA Kangwei Liu,et al."GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs".International Journal of Computer Vision 121.3(2017):365-390.
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