| A Doubly Graduated Method for Inference in Markov Random Field\ast | |
Yang, Xu1,2 ; Liu, Zhi-Yong1,2
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| Source Publication | SIAM JOURNAL ON IMAGING SCIENCES
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| ISSN | 1936-4954 |
| 2021 | |
| Volume | 14Issue:3Pages:1354-1373 |
| Corresponding Author | Yang, Xu(xu.yang@ia.ac.cn) |
| Abstract | Maximum a posteriori (MAP) inference in Markov random field (MRF) lays the foundation for many computer vision tasks, which can be formulated by a binary quadratic programming (BQP) problem. Compared with the discrete methods, the continuous relaxation scheme becomes popular due to its generality and efficiency. However, existing continuous relaxation based MAP algorithms are still limited by two problems, i.e., the highly nonconvex original objective function and the gap between the original BQP problem and the relaxed continuous optimization problem. Targeting the two problems, this paper presents a doubly graduated continuous relaxation algorithm for MAP inference in MRF, which are, respectively, the Gaussian smoothing based graduated nonconvexity process and conditional gradient ascent based graduated projection. Experiments on both synthetic data and real-world images illustrate the algorithm's state-of-the-art performance in objective function optimization and typical computer vision tasks. |
| Keyword | maximum a posteriori Markov random field Gaussian smoothing continuous relaxation |
| DOI | 10.1137/20M1383574 |
| WOS Keyword | ENERGY MINIMIZATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation (NSFC) of China[61973301] ; National Natural Science Foundation (NSFC) of China[61972020] ; National Natural Science Foundation (NSFC) of China[61633009] ; National Key R&D Program of China[2020AAA0108902] ; Beijing Science and Technology Plan Project[Z201100008320029] |
| Funding Organization | National Natural Science Foundation (NSFC) of China ; National Key R&D Program of China ; Beijing Science and Technology Plan Project |
| WOS Research Area | Computer Science ; Mathematics ; Imaging Science & Photographic Technology |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Mathematics, Applied ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000735767700006 |
| Publisher | SIAM PUBLICATIONS |
| Sub direction classification | 模式识别基础 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47122 |
| Collection | 复杂系统管理与控制国家重点实验室_机器人理论与应用 |
| Corresponding Author | Yang, Xu |
| Affiliation | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Yang, Xu,Liu, Zhi-Yong. A Doubly Graduated Method for Inference in Markov Random Field\ast[J]. SIAM JOURNAL ON IMAGING SCIENCES,2021,14(3):1354-1373. |
| APA | Yang, Xu,&Liu, Zhi-Yong.(2021).A Doubly Graduated Method for Inference in Markov Random Field\ast.SIAM JOURNAL ON IMAGING SCIENCES,14(3),1354-1373. |
| MLA | Yang, Xu,et al."A Doubly Graduated Method for Inference in Markov Random Field\ast".SIAM JOURNAL ON IMAGING SCIENCES 14.3(2021):1354-1373. |
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