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

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

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.

Citation

Gao, F., Xue, X., Wang, J., Sun, J., Hussain, A., & Yang, E. (2016, November). Visual attention model with a novel learning strategy and its application to target detection from SAR images. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: BICS 2016: International Conference on Brain Inspired Cognitive Systems
• Start Date: Nov 28, 2016
• End Date: Nov 30, 2016
• Online Publication Date: Nov 13, 2016
• Publication Date: 2016
• Deposit Date: Oct 4, 2019
• Publisher: Springer
• Pages: 149-160
• Series Title: Lecture Notes in Computer Science
• Series Number: 10023
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems
• ISBN: 978-3-319-49684-9
• DOI: https://doi.org/10.1007/978-3-319-49685-6_14
• Keywords: Visual attention model; Object detection; Learning strategy; Synthetic Aperture Radar (SAR) images
• Public URL: http://researchrepository.napier.ac.uk/Output/1792788