Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis
文献类型：期刊论文
| 作者 | Liu, Risheng1; Zhong, Guangyu2; Cao, Junjie2; Lin, Zhouchen3,4; Shan, Shiguang5; Luo, Zhongxuan1 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2016-12-01 |
| 卷号 | 38期号:12页码:2457-2471 |
| 关键词 | Visual diffusion PDE governed combinatorial optimization submodularity saliency detection object tracking |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2016.2522415 |
| 英文摘要 | Partial differential equations (PDEs) have been used to formulate image processing for several decades. Generally, a PDE system consists of two components: the governing equation and the boundary condition. In most previous work, both of them are generally designed by people using mathematical skills. However, in real world visual analysis tasks, such predefined and fixed-form PDEs may not be able to describe the complex structure of the visual data. More importantly, it is hard to incorporate the labeling information and the discriminative distribution priors into these PDEs. To address above issues, we propose a new PDE framework, named learning to diffuse (LTD), to adaptively design the governing equation and the boundary condition of a diffusion PDE system for various vision tasks on different types of visual data. To our best knowledge, the problems considered in this paper (i.e., saliency detection and object tracking) have never been addressed by PDE models before. Experimental results on various challenging benchmark databases show the superiority of LTD against existing state-of-the-art methods for all the tested visual analysis tasks. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[61300086] ; National Natural Science Foundation of China (NSFC)[61432003] ; Fundamental Research Funds for the Central Universities[DUT15QY15] ; Hong Kong Scholar Program[XJ2015008] ; China Scholarship Council ; NSFC[61363048] ; NSFC[61272341] ; NSFC[61231002] ; NSFC[61222211] ; National Basic Research Program of China (973 Program)[2015CB352502] ; Microsoft Research Asia Collaborative Research Program |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000387984700009 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/7894]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Risheng |
| 作者单位 | 1.Dalian Univ Technol, Sch Software Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China 2.Dalian Univ Technol, Sch Math Sci, Dalian, Peoples R China 3.Peking Univ, Sch Elect Engn & Comp Sci, Key Lab Machine Percept MOE, Beijing, Peoples R China 4.Shanghai Jiao Tong Univ, Cooperat Medianet Innovat Ctr, Shanghai, Peoples R China 5.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Risheng,Zhong, Guangyu,Cao, Junjie,et al. Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2016,38(12):2457-2471. |
| APA | Liu, Risheng,Zhong, Guangyu,Cao, Junjie,Lin, Zhouchen,Shan, Shiguang,&Luo, Zhongxuan.(2016).Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,38(12),2457-2471. |
| MLA | Liu, Risheng,et al."Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38.12(2016):2457-2471. |
入库方式： OAI收割
来源：计算技术研究所
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