Hebbian-Based Neural Networks for Bottom-Up Visual Attention Systems

Authors:
Ying Yu;Bin Wang;Liming Zhang
Affiliations:
Department of Electronic Engineering, Fudan University, Shanghai, P.R. China 200433;Department of Electronic Engineering, Fudan University, Shanghai, P.R. China 200433 and The Key Lab of Wave Scattering and Remote Sensing Information (Ministry of Education), Fudan University, Sha ...;Department of Electronic Engineering, Fudan University, Shanghai, P.R. China 200433
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Year:
2009

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Abstract

This paper proposes a bottom-up attention model based on pulsed Hebbian-based neural networks that simulate the lateral surround inhibition of neurons with similar visual features. The visual saliency can be represented in binary codes that simulate neuronal pulses in the human brain. Moreover, the model can be extended to the pulsed cosine transform that is very simple in computation. Finally, a dynamic Markov model is proposed to produce the human-like stochastic attention selection. Due to its good performance in eye fixation prediction and low computational complexity, our model can be used in real-time systems such as robot navigation and virtual human system.