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Sparse-Distinctive Saliency Detection
Luo, Yongkang1; Wang, Peng1; Zhu, Wenjun1; Qiao, Hong2
Source PublicationIEEE SIGNAL PROCESSING LETTERS
2015-09-01
Volume22Issue:9Pages:1378-1382
SubtypeArticle
AbstractIn this letter, we propose a novel saliency model for saliency detection, named sparse-distinctive (SD) saliency model. Different from the existing models that only consider sparsity or distinctness of image, the proposed model computes saliency based on sparsity and distinctness. The basic idea is that sparsity and distinctness contribute to saliency simultaneously and play different roles under different scenes. This sparse-distinctive saliency model is based on some key ideas introduced in this letter and supported by psychological evidence. Experimental results on public benchmark eye-tracking datasets show that considering the sparsity and distinctness for saliency can improve the accuracy of predicting human fixations, and the proposed model outperforms the mainstream models on predicting human fixations.
KeywordDistinctness Saliency Sparsity Visual Attention
WOS HeadingsScience & Technology ; Technology
WOS KeywordDistinctness, saliency, sparsity, visual attention
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000358598800005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8880
Collection精密感知与控制研究中心_精密感知与控制
Affiliation1.The Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences
2.The State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Luo, Yongkang,Wang, Peng,Zhu, Wenjun,et al. Sparse-Distinctive Saliency Detection[J]. IEEE SIGNAL PROCESSING LETTERS,2015,22(9):1378-1382.
APA Luo, Yongkang,Wang, Peng,Zhu, Wenjun,&Qiao, Hong.(2015).Sparse-Distinctive Saliency Detection.IEEE SIGNAL PROCESSING LETTERS,22(9),1378-1382.
MLA Luo, Yongkang,et al."Sparse-Distinctive Saliency Detection".IEEE SIGNAL PROCESSING LETTERS 22.9(2015):1378-1382.
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