GRMA: Generalized Range Move Algorithms for the Efficient Optimization of MRFs
文献类型：期刊论文
| 作者 | Kangwei Liu1 ; Junge Zhang1; Peipei Yang1; Stephen Maybank2; Kaiqi Huang1
|
| 刊名 | International Journal of Computer Vision
 |
| 出版日期 | 2017 |
| 卷号 | 121期号:3页码:365-390 |
| 关键词 | Markov Random Field Discrete Optimization Range Move Algorithms |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12423]  |
| 专题 | 自动化研究所_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. |
入库方式： OAI收割
来源：自动化研究所
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