Two-Stage Learning to Predict Human Eye Fixations via SDAEs
文献类型：期刊论文
| 作者 | Han, Junwei1; Zhang, Dingwen1; Wen, Shifeng1; Guo, Lei1; Liu, Tianming2; Li, Xuelong3
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-02-01 |
| 卷号 | 46期号:2页码:487-498 |
| 关键词 | Deep networks eye fixation prediction saliency detection stacked denoising autoencoders ( SDAEs) |
| ISSN号 | 2168-2267 |
| 产权排序 | 3 |
| 通讯作者 | han, jw |
| 英文摘要 | 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. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | remote-sensing images ; visual saliency ; object detection ; attention ; retrieval ; model ; representations ; autoencoders ; framework ; regions |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000370962900014 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27859]  |
| 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.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,&Li, Xuelong.(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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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