CASIA OpenIR  > 智能感知与计算研究中心
GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs
Kangwei Liu1; Junge Zhang1; Peipei Yang1; Stephen Maybank2; Kaiqi Huang1
2017
发表期刊International Journal of Computer Vision
卷号121期号:3页码:365-390
摘要Markov 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.

关键词Markov Random Field Discrete Optimization Range Move Algorithms
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12423
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位1.CASIA
2.Birkbeck College, University of London
推荐引用方式
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
IJCV.pdf(1430KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Kangwei Liu]的文章
[Junge Zhang]的文章
[Peipei Yang]的文章
百度学术
百度学术中相似的文章
[Kangwei Liu]的文章
[Junge Zhang]的文章
[Peipei Yang]的文章
必应学术
必应学术中相似的文章
[Kangwei Liu]的文章
[Junge Zhang]的文章
[Peipei Yang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: IJCV.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。