NDLI: GRSA: Generalized range swap algorithm for the efficient optimization of MRFs

Content Provider	IEEE Xplore Digital Library
Author	Kangwei Liu Junge Zhang Peipei Yang Kaiqi Huang
Copyright Year	2015
Description	Author affiliation: Center for Res. on Intell. Perception & Comput., Inst. of Autom., Beijing, China (Kangwei Liu; Junge Zhang; Peipei Yang; Kaiqi Huang)
Abstract	Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. However, two problems have limited the applicability of previous range move algorithms: 1) They are limited in the types of energies they can handle (i.e. only truncated convex functions); 2) These algorithms tend to be very slow compared to other graph-cut based algorithms (e.g. α-expansion and αβ-swap). In this paper, we propose a generalized range swap algorithm (GRSA) for efficient optimization of MRFs. To address the first problem, we extend the GRSA to arbitrary semimetric energies by restricting the chosen labels in each move so that the energy is submodular on the chosen subset. Furthermore, to feasibly choose the labels satisfying the submodular condition, we provide a sufficient condition of the submodularity. For the second problem, unlike previous range move algorithms which execute the set of all possible range moves, we dynamically obtain the iterative moves by solving a set cover problem, which greatly reduces the number of moves during the optimization. Experiments show that the GRSA offers a great speedup over previous range swap algorithms, while it obtains competitive solutions.
Starting Page	1761
Ending Page	1769
File Size	2118764
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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