Visual saliency detection using information divergence

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Visual saliency detection using information divergence
	Hou, Weilong 1; Gao, Xinbo 1; Tao, Dacheng2,3 ; Li, Xuelong4
刊名	pattern recognition
	2013-10-01
卷号	46 期号:10 页码:2658-2669
关键词	Visual attention Saliency detection Independent component analysis Bayesian surprise model
英文摘要	the technique of visual saliency detection supports video surveillance systems by reducing redundant information and highlighting the critical, visually important regions. it follows that information about the image might be of great importance in depicting the visual saliency. however, the majority of existing methods extract contrast-like features without considering the contribution of information content. based on the hypothesis that information divergence leads to visual saliency, a two-stage framework for saliency detection, namely information divergence model (idm), is introduced in this paper. the term "information divergence" is used to express the non-uniform distribution of the visual information in an image. the first stage is constructed to extract sparse features by employing independent component analysis (ica) and difference of gaussians (dog) filter. the second stage improves the bayesian surprise model to compute information divergence across an image. a visual saliency map is finally obtained from the information divergence. experiments are conducted on nature image databases, psychological patterns and video surveillance sequences. the results show the effectiveness of the proposed method by comparing it with 13 state-of-the-art visual saliency detection methods. (c) 2013 elsevier ltd. all rights reserved.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	sparse representation ; neurobiological model ; image retrieval ; natural scenes ; simple cells ; attention ; features ; filters ; overt ; recognition
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000320477400005
公开日期	2015-06-30
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/23444]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Hou, Weilong,Gao, Xinbo,Tao, Dacheng,et al. Visual saliency detection using information divergence[J]. pattern recognition,2013,46(10):2658-2669.
APA	Hou, Weilong,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2013).Visual saliency detection using information divergence.pattern recognition,46(10),2658-2669.
MLA	Hou, Weilong,et al."Visual saliency detection using information divergence".pattern recognition 46.10(2013):2658-2669.