Selective Visual Attention Model Based on Pulsed Cosine Transform

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.