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Learning visual saliency from human fixations for stereoscopic images
Fang, Yuming1; Lei, Jianjun2; Li, Jia3; Xu, Long4; Lin, Weisi5; Le Callet, Patrick6
2017-11-29
发表期刊NEUROCOMPUTING
卷号266页码:284-292
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.
文章类型Article
关键词Stereoscopic Image 3d Image Stereoscopic Saliency Detection Visual Attention Machine Learning Support Vector Regression
WOS标题词Science & Technology ; Technology
资助者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)
DOI10.1016/j.neucom.2017.05.050
关键词[WOS]ATTENTION ; MODEL ; FEATURES ; SCENE ; RECOGNITION ; MECHANISMS ; VIDEOS
收录类别SCI
语种英语
资助者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)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000408183900027
引用统计
文献类型期刊论文
条目标识符http://ir.bao.ac.cn/handle/114a11/20132
专题太阳物理研究部
作者单位1.Jiangxi Univ Finance & Econ, Sch Informat Technol, Jiangxi Prov Key Lab Digital Media, Nanchang 330032, Jiangxi, Peoples R China
2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
3.Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
4.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
5.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
6.Univ Nantes, Polytech Nantes, LUNAM Univ, CNRS,IRCCyN,UMR 6597, Nantes, France
推荐引用方式
GB/T 7714
Fang, Yuming,Lei, Jianjun,Li, Jia,et al. Learning visual saliency from human fixations for stereoscopic images[J]. NEUROCOMPUTING,2017,266:284-292.
APA Fang, Yuming,Lei, Jianjun,Li, Jia,Xu, Long,Lin, Weisi,&Le Callet, Patrick.(2017).Learning visual saliency from human fixations for stereoscopic images.NEUROCOMPUTING,266,284-292.
MLA Fang, Yuming,et al."Learning visual saliency from human fixations for stereoscopic images".NEUROCOMPUTING 266(2017):284-292.
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