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

Gao, Fei and Xue, Xiangshang and Wang, Jun and Sun, Jinping and Hussain, Amir and Yang, Erfu; Liu, Cheng-Lin and Hussain, Amir and Luo, Bin and Tan, Kay Chen and Zeng, Yi and Zhang, Zhaoxiang, eds. (2016) Visual attention model with a novel learning strategy and its application to target detection from SAR images. In: Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag, CHN, pp. 149-160. ISBN 9783319496849

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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.

    ORCID iDs

    Gao, Fei, Xue, Xiangshang, Wang, Jun, Sun, Jinping, Hussain, Amir and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950; Liu, Cheng-Lin, Hussain, Amir, Luo, Bin, Tan, Kay Chen, Zeng, Yi and Zhang, Zhaoxiang