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Learning visual saliency from human fixations for stereoscopic images

文献类型：期刊论文

作者Li, Jia1; Lei, Jianjun6; Fang, Yuming2; Le Callet, Patrick3; Lin, Weisi4; Xu, Long5
刊名NEUROCOMPUTING
出版日期2017-11-29
卷号266页码:284-292
关键词Stereoscopic Image 3d Image Stereoscopic Saliency Detection Visual Attention Machine Learning Support Vector Regression
DOI10.1016/j.neucom.2017.05.050
文献子类Article
英文摘要In the previous years, a lot of saliency detection algorithms have been designed for saliency computation of visual content. Recently, stereoscopic display techniques have developed rapidly, which results in much requirement of stereoscopic saliency detection for emerging stereoscopic applications. Different from 2D saliency prediction, stereoscopic saliency detection methods have to consider depth factor. We design a novel stereoscopic saliency detection algorithm by machine learning technique. First, the features of luminance, color and texture are extracted to calculate the feature contract for predicting feature maps of stereoscopic images. Furthermore, the depth features are extracted for depth feature map computation. Sematic features including the center-bias factor and other top-down cues are also applied as the features in the proposed stereoscopic saliency detection method. Support Vector Regression (SVR) is applied to learn the saliency detection model of stereoscopic images. Experimental results obtained on a public large-scale eye tracking database demonstrate that the proposed method can predict better saliency results for stereoscopic images than other existing ones. (C) 2017 Elsevier B.V. All rights reserved.
WOS关键词ATTENTION ; MODEL ; FEATURES ; SCENE ; RECOGNITION ; MECHANISMS ; VIDEOS
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000408183900027
资助机构Natural Science Foundation of China(61571212) ; Natural Science Foundation of China(61571212) ; Natural Science Foundation of Jiangxi Province in China(GJJ160420 ; Natural Science Foundation of Jiangxi Province in China(GJJ160420 ; 20161ACB21014 ; 20161ACB21014 ; 20151BDH80003) ; 20151BDH80003) ; Natural Science Foundation of China(61571212) ; Natural Science Foundation of China(61571212) ; Natural Science Foundation of Jiangxi Province in China(GJJ160420 ; Natural Science Foundation of Jiangxi Province in China(GJJ160420 ; 20161ACB21014 ; 20161ACB21014 ; 20151BDH80003) ; 20151BDH80003)
源URL[http://ir.bao.ac.cn/handle/114a11/20132]  
专题国家天文台_太阳物理研究部
作者单位1.Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
2.Jiangxi Univ Finance & Econ, Sch Informat Technol, Jiangxi Prov Key Lab Digital Media, Nanchang 330032, Jiangxi, Peoples R China
3.Univ Nantes, Polytech Nantes, LUNAM Univ, CNRS,IRCCyN,UMR 6597, Nantes, France
4.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
5.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
6.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jia,Lei, Jianjun,Fang, Yuming,et al. Learning visual saliency from human fixations for stereoscopic images[J]. NEUROCOMPUTING,2017,266:284-292.
APA Li, Jia,Lei, Jianjun,Fang, Yuming,Le Callet, Patrick,Lin, Weisi,&Xu, Long.(2017).Learning visual saliency from human fixations for stereoscopic images.NEUROCOMPUTING,266,284-292.
MLA Li, Jia,et al."Learning visual saliency from human fixations for stereoscopic images".NEUROCOMPUTING 266(2017):284-292.

入库方式： OAI收割

来源：国家天文台

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