Hierarchical Visual Attention Model for Saliency Detection Inspired by Avian Visual Pathways
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  • 英文篇名:Hierarchical Visual Attention Model for Saliency Detection Inspired by Avian Visual Pathways
  • 作者:Xiaohua ; Wang ; Haibin ; Duan
  • 英文作者:Xiaohua Wang;Haibin Duan;the State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University;IEEE;the State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering,Beihang University;the Peng Cheng Laboratory;
  • 英文关键词:Avian visual pathways;;bio-inspired;;saliency detection;;visual attention
  • 中文刊名:ZDHB
  • 英文刊名:自动化学报(英文版)
  • 机构:the State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University;IEEE;the State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering,Beihang University;the Peng Cheng Laboratory;
  • 出版日期:2019-03-15
  • 出版单位:IEEE/CAA Journal of Automatica Sinica
  • 年:2019
  • 期:v.6
  • 基金:supported by Natural Science Foundation of China(61425008,61333004,61273054)
  • 语种:英文;
  • 页:ZDHB201902018
  • 页数:13
  • CN:02
  • ISSN:10-1193/TP
  • 分类号:207-219
摘要
Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment. Most computational visual attention models are designed with inspirations from mammalian visual systems.However, electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time. Therefore,the visual system of the avian species, especially the nuclei related to the visual attention mechanism, are investigated in this paper. Afterwards, a hierarchical visual attention model is proposed for saliency detection. The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy. The "winner-takeall" network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy. Comparison results verify that the proposed model, which can define the focus of attention accurately, outperforms several state-of-the-art models.This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways. The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.
        Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment. Most computational visual attention models are designed with inspirations from mammalian visual systems.However, electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time. Therefore,the visual system of the avian species, especially the nuclei related to the visual attention mechanism, are investigated in this paper. Afterwards, a hierarchical visual attention model is proposed for saliency detection. The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy. The "winner-takeall" network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy. Comparison results verify that the proposed model, which can define the focus of attention accurately, outperforms several state-of-the-art models.This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways. The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.
引文
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