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. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
Robust Visual Saliency Optimization Based on Bidirectional Markov Chains

文献类型：期刊论文

作者Jiang, Fengling1,2,3; Kong, Bin2,4; Li, Jingpeng5; Dashtipour, Kia5; Gogate, Mandar6
刊名COGNITIVE COMPUTATION
出版日期2020-05-29
关键词Saliency detection Bidirectional absorbing Markov chain Background and foreground possibility
ISSN号1866-9956
DOI10.1007/s12559-020-09724-6
通讯作者Kong, Bin(bkong@iim.ac.cn)
英文摘要Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent saliency detection method based on the bidirectional absorbing Markov chains that jointly exploits not only the boundary information but also the foreground prior and background prior cues. More specifically, the input image is first segmented into number of superpixels, and the four boundary nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node's random walk to the absorbing state is calculated to obtain the foreground possibility. Simultaneously, foreground prior (as the virtual absorbing nodes) is used to calculate the absorption time and get the background possibility. In addition, the two aforementioned results are fused to form a combined saliency map which is further optimized by using a cost function. Finally, the superpixel-level saliency results are optimized by a regularized random walks ranking model at multi-scale. The comparative experimental results on four benchmark datasets reveal superior performance of our proposed method over state-of-the-art methods reported in the literature. The experiments show that the proposed method is efficient and can be applicable to the bottom-up image saliency detection and other visual processing tasks.
WOS关键词BOTTOM-UP ; TOP-DOWN ; OBJECT DETECTION ; INFORMATION ; ATTENTION ; MODEL
资助项目China Scholarship Council ; National Natural Science Foundation of China[913203002] ; Pilot Project of Chinese Academy of Sciences[XDA08040109] ; Fundamental Research Funds for the Central Universities of China[ACAIM190302] ; Universities Joint Key Laboratory of Photoelectric Detection Science and Technology in Anhui Province[2019GDTCZD02]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000536324300001
出版者SPRINGER
资助机构China Scholarship Council ; National Natural Science Foundation of China ; Pilot Project of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities of China ; Universities Joint Key Laboratory of Photoelectric Detection Science and Technology in Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/103169]  
专题中国科学院合肥物质科学研究院
通讯作者Kong, Bin
作者单位1.Univ Sci & Technol China, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
3.Hefei Normal Univ, Sch Comp Sci & Technol, Hefei 230061, Peoples R China
4.Key Lab Biomimet Sensing & Adv Robot Technol, Hefei 230031, Peoples R China
5.Univ Stirling, Div Comp Sci & Math, Stirling FK9 4LA, Scotland
6.Edinburgh Napier Univ, Sch Comp, Edinburgh EH10 5DT, Midlothian, Scotland
推荐引用方式
GB/T 7714		 Jiang, Fengling,Kong, Bin,Li, Jingpeng,et al. Robust Visual Saliency Optimization Based on Bidirectional Markov Chains[J]. COGNITIVE COMPUTATION,2020.
APA Jiang, Fengling,Kong, Bin,Li, Jingpeng,Dashtipour, Kia,&Gogate, Mandar.(2020).Robust Visual Saliency Optimization Based on Bidirectional Markov Chains.COGNITIVE COMPUTATION.
MLA Jiang, Fengling,et al."Robust Visual Saliency Optimization Based on Bidirectional Markov Chains".COGNITIVE COMPUTATION (2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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。