Weakly Supervised Human Fixations Prediction

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

	Weakly Supervised Human Fixations Prediction
	Zhang, Luming 6; Li, Xuelong7 ; Nie, Liqiang 8; Yang, Yi 9; Xia, Yingjie 10
刊名	ieee transactions on cybernetics
	2016
卷号	46 期号:1 页码:258-269
关键词	Attention computer vision graphlets machine learning manifold embedding weakly supervised
ISSN号	2168-2267
通讯作者	xia, yj
产权排序	2
英文摘要	automatically predicting human eye fixations is a useful technique that can facilitate many multimedia applications, e.g., image retrieval, action recognition, and photo retargeting. conventional approaches are frustrated by two drawbacks. first, psychophysical experiments show that an object-level interpretation of scenes influences eye movements significantly. most of the existing saliency models rely on object detectors, and therefore, only a few prespecified categories can be discovered. second, the relative displacement of objects influences their saliency remarkably, but current models cannot describe them explicitly. to solve these problems, this paper proposes weakly supervised fixations prediction, which leverages image labels to improve accuracy of human fixations prediction. the proposed model hierarchically discovers objects as well as their spatial configurations. starting from the raw image pixels, we sample superpixels in an image, thereby seamless object descriptors termed object-level graphlets (ogls) are generated by random walking on the superpixel mosaic. then, a manifold embedding algorithm is proposed to encode image labels into ogls, and the response map of each prespecified object is computed accordingly. on the basis of the object-level response map, we propose spatial-level graphlets (sgls) to model the relative positions among objects. afterward, eye tracking data is employed to integrate these sgls for predicting human eye fixations. thorough experiment results demonstrate the advantage of the proposed method over the state-of-the-art.
学科主题	computer science, artificial intelligence ; computer science, cybernetics
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	saliency detection ; visual saliency ; image segmentation ; model ; recognition ; gradients ; attention ; contrast ; scene
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000367144300023
公开日期	2016-02-25
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27737]
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.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 3.Natl Univ Singapore, Sch Comp, Singapore 119613, Singapore 4.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 5.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China 6.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 7.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 8.Natl Univ Singapore, Sch Comp, Singapore 119613, Singapore 9.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 10.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Luming,Li, Xuelong,Nie, Liqiang,et al. Weakly Supervised Human Fixations Prediction[J]. ieee transactions on cybernetics,2016,46(1):258-269.
APA	Zhang, Luming,Li, Xuelong,Nie, Liqiang,Yang, Yi,&Xia, Yingjie.(2016).Weakly Supervised Human Fixations Prediction.ieee transactions on cybernetics,46(1),258-269.
MLA	Zhang, Luming,et al."Weakly Supervised Human Fixations Prediction".ieee transactions on cybernetics 46.1(2016):258-269.