Cost-Sensitive Rank Learning From Positive and Unlabeled Data for Visual   Saliency Estimation

doi:10.1109/LSP.2010.2048049

CORC > 北京大学 > 信息科学技术学院

	Cost-Sensitive Rank Learning From Positive and Unlabeled Data for Visual Saliency Estimation
	Li, Jia ; Tian, Yonghong ; Huang, Tiejun ; Gao, Wen
刊名	ieee signal processing letters
	2010
关键词	Cost-sensitive positive and unlabeled data rank learning visual saliency ATTENTION
DOI	10.1109/LSP.2010.2048049
英文摘要	This paper presents a cost-sensitive rank learning approach for visual saliency estimation. This approach avoids the explicit selection of positive and negative samples, which is often used by existing learning-based visual saliency estimation approaches. Instead, both the positive and unlabeled data are directly integrated into a rank learning framework in a cost-sensitive manner. Compared with existing approaches, the rank learning framework can take the influences of both the local visual attributes and the pair-wise contexts into account simultaneously. Experimental results show that our algorithm outperforms several state-of-the-art approaches remarkably in visual saliency estimation.; Engineering, Electrical & Electronic; SCI(E); EI; 2; ARTICLE; 6; 591-594; 17
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/243702]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Jia,Tian, Yonghong,Huang, Tiejun,et al. Cost-Sensitive Rank Learning From Positive and Unlabeled Data for Visual Saliency Estimation[J]. ieee signal processing letters,2010.
APA	Li, Jia,Tian, Yonghong,Huang, Tiejun,&Gao, Wen.(2010).Cost-Sensitive Rank Learning From Positive and Unlabeled Data for Visual Saliency Estimation.ieee signal processing letters.
MLA	Li, Jia,et al."Cost-Sensitive Rank Learning From Positive and Unlabeled Data for Visual Saliency Estimation".ieee signal processing letters (2010).