中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 机器人应用与理论组
A Doubly Graduated Method for Inference in Markov Random Field\ast

文献类型：期刊论文

作者Yang, Xu1,2; Liu, Zhi-Yong1,2
刊名SIAM JOURNAL ON IMAGING SCIENCES
出版日期2021
卷号14期号:3页码:1354-1373
关键词maximum a posteriori Markov random field Gaussian smoothing continuous relaxation
ISSN号1936-4954
DOI10.1137/20M1383574
通讯作者Yang, Xu(xu.yang@ia.ac.cn)
英文摘要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.
WOS关键词ENERGY MINIMIZATION
资助项目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]
WOS研究方向Computer Science ; Mathematics ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000735767700006
出版者SIAM PUBLICATIONS
资助机构National Natural Science Foundation (NSFC) of China ; National Key R&D Program of China ; Beijing Science and Technology Plan Project
源URL[http://ir.ia.ac.cn/handle/173211/47121]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Yang, Xu
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

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。