Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images

Authors:
Ying Yu;Bin Wang;Liming Zhang
Affiliations:
School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China and Department of Electronic Engineering, Fudan University, Shanghai 200433, PR China;Department of Electronic Engineering, Fudan University, Shanghai 200433, PR China and The Key Lab of Wave Scattering and Remote Sensing Information (Ministry of Education), Fudan University, Shang ...;Department of Electronic Engineering, Fudan University, Shanghai 200433, PR China
Venue:
Neurocomputing
Year:
2011

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Abstract

This paper proposes a bottom-up attention model based on pulsed Hebbian neural networks. The salience of the visual input can be generated through the networks using a simple normalization process, which can be calculated rapidly. Moreover, visual salience in this model can be represented as binary codes that mimic neuronal pulses in the human brain. Experimental results on psychophysical patterns and eye fixation prediction for natural images prove the effectiveness and efficiency of the model. In an arduous task of detecting ships in synthetic aperture radar (SAR) images, there are large amounts of data to be processed in real time. As a fast and effective technique for saliency detection, the proposed model is applied to ship detection in SAR images and its robustness against speckles is further proved.