Knowledge Commons of Institute of Automation,CAS
A Doubly Graduated Method for Inference in Markov Random Field\ast | |
Yang, Xu1,2; Liu, Zhi-Yong1,2 | |
发表期刊 | SIAM JOURNAL ON IMAGING SCIENCES |
ISSN | 1936-4954 |
2021 | |
卷号 | 14期号:3页码:1354-1373 |
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. |
关键词 | maximum a posteriori Markov random field Gaussian smoothing continuous relaxation |
DOI | 10.1137/20M1383574 |
关键词[WOS] | ENERGY MINIMIZATION |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Natural Science Foundation (NSFC) of China ; National Key R&D Program of China ; Beijing Science and Technology Plan Project |
WOS研究方向 | Computer Science ; Mathematics ; Imaging Science & Photographic Technology |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Mathematics, Applied ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000735767700006 |
出版者 | SIAM PUBLICATIONS |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47122 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Yang, Xu |
作者单位 | 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 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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 | ||
A Doubly Graduated M(993KB) | 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 |
[Yang, Xu]的文章 |
[Liu, Zhi-Yong]的文章 |
百度学术 |
百度学术中相似的文章 |
[Yang, Xu]的文章 |
[Liu, Zhi-Yong]的文章 |
必应学术 |
必应学术中相似的文章 |
[Yang, Xu]的文章 |
[Liu, Zhi-Yong]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。
修改评论