A behaviorally inspired fusion approach for computational audiovisual saliency modeling
作者：
Highlights：
• Audiovisual bottom-up human attention modeling via computational audiovisual saliency
• Three different generic audiovisual fusion schemes resulting in 2-D saliency map
• Audiovisual eye-tracking data collection for SumMe, ETMD databases, publicly released
• Evaluation by comparison with human experimental findings from behavioral experiments
• Evaluation with 6 eye-tracking databases: DIEM, AVAD, Coutrot1, Coutrot2, SumMe, ETMD
摘要
•Audiovisual bottom-up human attention modeling via computational audiovisual saliency•Three different generic audiovisual fusion schemes resulting in 2-D saliency map•Audiovisual eye-tracking data collection for SumMe, ETMD databases, publicly released•Evaluation by comparison with human experimental findings from behavioral experiments•Evaluation with 6 eye-tracking databases: DIEM, AVAD, Coutrot1, Coutrot2, SumMe, ETMD
论文关键词：Audiovisual saliency,Attention,Fusion,Eye-tracking
论文评审过程：Received 28 December 2018, Revised 1 April 2019, Accepted 2 May 2019, Available online 20 May 2019, Version of Record 20 May 2019.
论文官网地址：https://doi.org/10.1016/j.image.2019.05.001
