Selective Visual Attention Model Based on Pulsed Cosine Transform
YU Ying1,WANG Bin1,2,ZHANG Li-Ming1
1.Department of Electronic Engineering,School of Information Science and Engineering,Fudan University,Shanghai 200433 2.Key Laboratory of Wave Scattering and Remote Sensing Information Ministry of Education,Fudan University,Shanghai 200433
Abstract：A visual attention model based on pulsed cosine transform is proposed, which mimics the generating mechanism of bottom-up visual attention. Due to its simple architecture and high computational speed, the proposed model can be used in real-time systems. The visual salience of the model is represented in binary codes, which agrees with the firing pattern of neurons in the human brain. The motion salience is generated by these binary codes as well. Moreover, the model can be extended to Hebbian-based neural networks. Experimental results show that the proposed model has better performance in human fixation prediction than other state-of-the-art models of visual attention.
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2010, Vol. 23 
