ShanghaiTech University Knowledge Management System
Personalized Saliency and Its Prediction | |
2019-12 | |
发表期刊 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
ISSN | 0162-8828 |
EISSN | 1939-3539 |
卷号 | 41期号:12页码:2975-2989 |
发表状态 | 已发表 |
DOI | 10.1109/TPAMI.2018.2866563 |
摘要 | Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstances, especially a scene is composed of multiple salient objects. To study such heterogenous visual attention pattern across observers, we first construct a personalized saliency dataset and explore correlations between visual attention, personal preferences, and image contents. Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency. We then present two solutions towards predicting such discrepancy maps, i.e., a multi-task convolutional neural network (CNN) framework and an extended CNN with Person-specific Information Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the effectiveness of our models for PSM prediction as well their generalization capability for unseen observers. |
关键词 | Observers Saliency detection Feature extraction Visualization Semantics Predictive models Image color analysis Universal saliency personalized saliency multi-task learning convolutional neural network |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | US National Science Foundation[HCC-1319598] ; US National Science Foundation[IIS-1422477] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000498677600014 |
出版者 | IEEE COMPUTER SOC |
WOS关键词 | MODEL |
原始文献类型 | Article |
来源库 | IEEE |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/28257 |
专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_虞晶怡组 信息科学与技术学院_PI研究组_高盛华组 |
作者单位 | 1.ShanghaiTech University, Pudong, China 2.Texas A&M University, College Station 3.University of Delaware, Newark, USA |
第一作者单位 | 上海科技大学 |
第一作者的第一单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Yanyu Xu,Shenghua Gao,Junru Wu,et al. Personalized Saliency and Its Prediction[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2019,41(12):2975-2989. |
APA | Yanyu Xu,Shenghua Gao,Junru Wu,Nianyi Li,&Jingyi Yu.(2019).Personalized Saliency and Its Prediction.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,41(12),2975-2989. |
MLA | Yanyu Xu,et al."Personalized Saliency and Its Prediction".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 41.12(2019):2975-2989. |
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