Visual attention model with a novel learning strategy and its application to target detection from SAR images

Fei Gao, Xiangshang Xue, Jun Wang, Jinping Sun, Amir Hussain, Erfu Yang

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Abstract

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
Subtitle of host publication8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
EditorsCheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang
Place of PublicationCham, Switzerland
PublisherSpringer-Verlag
Pages149-160
Number of pages12
ISBN (Print)9783319496849
DOIs
Publication statusPublished - 13 Nov 2016
Event8th International Conference on Brain Inspired Cognitive Systems, BICS 2016 - Beijing, China
Duration: 28 Nov 201630 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10023
ISSN (Print)0302 9743

Conference

Conference8th International Conference on Brain Inspired Cognitive Systems, BICS 2016
CountryChina
CityBeijing
Period28/11/1630/11/16

Keywords

  • learning strategy
  • object detection
  • Synthetic Aperture Radar (SAR) images
  • visual attention model
  • visual information
  • bottom-up
  • top-down
  • constant false alarm rate algorithm

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  • Cite this

    Gao, F., Xue, X., Wang, J., Sun, J., Hussain, A., & Yang, E. (2016). Visual attention model with a novel learning strategy and its application to target detection from SAR images. In C-L. Liu, A. Hussain, B. Luo, K. C. Tan, Y. Zeng, & Z. Zhang (Eds.), Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings (pp. 149-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10023). Springer-Verlag. https://doi.org/10.1007/978-3-319-49685-6_14