Two-Stage Learning to Predict Human Eye Fixations via SDAEs

doi:10.1109/TCYB.2015.2404432

OPT OpenIR > 光谱成像技术研究室

	Two-Stage Learning to Predict Human Eye Fixations via SDAEs
	Han, Junwei 1; Zhang, Dingwen 1; Wen, Shifeng 1; Guo, Lei 1; Liu, Tianming 2; Li, Xuelong3 ; Han, JW
作者部门	光学影像学习与分析中心
	2016-02-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:2 页码:487-498
产权排序	3
摘要	Saliency detection models aiming to quantitatively predict human eye-attended locations in the visual field have been receiving increasing research interest in recent years. Unlike traditional methods that rely on hand-designed features and contrast inference mechanisms, this paper proposes a novel framework to learn saliency detection models from raw image data using deep networks. The proposed framework mainly consists of two learning stages. At the first learning stage, we develop a stacked denoising autoencoder (SDAE) model to learn robust, representative features from raw image data under an unsupervised manner. The second learning stage aims to jointly learn optimal mechanisms to capture the intrinsic mutual patterns as the feature contrast and to integrate them for final saliency prediction. Given the input of pairs of a center patch and its surrounding patches represented by the features learned at the first stage, a SDAE network is trained under the supervision of eye fixation labels, which achieves both contrast inference and contrast integration simultaneously. Experiments on three publically available eye tracking benchmarks and the comparisons with 16 state-of-the-art approaches demonstrate the effectiveness of the proposed framework.
文章类型	Article
关键词	Deep Networks Eye Fixation Prediction Saliency Detection Stacked Denoising Autoencoders ( Sdaes)
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2404432
收录类别	SCI ; EI
关键词[WOS]	REMOTE-SENSING IMAGES ; VISUAL SALIENCY ; OBJECT DETECTION ; ATTENTION ; RETRIEVAL ; MODEL ; REPRESENTATIONS ; AUTOENCODERS ; FRAMEWORK ; REGIONS
语种	英语
WOS研究方向	Computer Science
项目资助者	National Science Foundation of China(61473231 ; Doctoral Fund of Ministry of Education of China(20136102110037) ; 61333017)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000370962900014
引用统计	被引频次：107[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27859
专题	光谱成像技术研究室
通讯作者	Han, JW
作者单位	1.Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China 2.Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Han, Junwei,Zhang, Dingwen,Wen, Shifeng,et al. Two-Stage Learning to Predict Human Eye Fixations via SDAEs[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(2):487-498.
APA	Han, Junwei.,Zhang, Dingwen.,Wen, Shifeng.,Guo, Lei.,Liu, Tianming.,...&Han, JW.(2016).Two-Stage Learning to Predict Human Eye Fixations via SDAEs.IEEE TRANSACTIONS ON CYBERNETICS,46(2),487-498.
MLA	Han, Junwei,et al."Two-Stage Learning to Predict Human Eye Fixations via SDAEs".IEEE TRANSACTIONS ON CYBERNETICS 46.2(2016):487-498.

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