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.
[1] Treisman A M, Gelade G. A Feature-Integration Theory of Attention. Cognitive Psychology, 1980, 12(1): 97-136 [2] Koch C, Ullman S. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology, 1985, 4(4): 219-227 [3] Desimone R, Duncan J. Neural Mechanisms of Selective Visual Attention. Annual Review of Neuroscience, 1995, 18: 193-222 [4] Crick F, Koch C. Constraints on Cortical and Thalamic Projections: the No-Strong-Loops Hypothesis. Nature, 1998, 391(6664): 245-250 [5] Itti L, Koch C, Niebur E. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259 [6] Bruce N D B, Tsotsos J K. Saliency, Attention, and Visual Search: An Information Theoretic Approach. Journal of Vision, 2009, 9(3): 1-24 [7] Guo Chenlei, Zhang Liming. A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression. IEEE Trans on Image Processing, 2010, 19(1): 185-198 [8] Li Zhaoping. A Saliency Map in Primary Visual Cortex. Trends in Cognitive Sciences, 2002, 6(1): 9-16 [9] Li Zhaoping. Theoretical Understanding of the Early Visual Processes by Data Compression and Data Selection. Network: Computation in Neural Systems, 2006, 17(4): 301-334 [10] Gonzalez R C, Woods R E. Digital Image Processing. 2nd Edition. Upper Saddle River, USA: Prentice Hall, 2002 [11] Ahmed N, Natarajan T, Rao K. Discrete Cosine Transform. IEEE Trans on Computers, 1974, 23(1): 90-93 [12] Oja E. A Simplified Neuron Model as a Principal Component Analyzer. Journal of Mathematical Biology, 1982, 15(3): 267-273 [13] Sanger T D. Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network. IEEE Trans on Neural Networks, 1989, 2(6): 459-473 [14] Golub G H, van Loan C F. Matrix Computation. 3rd Edition. Baltimore, USA: John Hopkins University Press,1996 [15] Cavanagh P. Attention-Based Motion Perception. Science, 1992, 257(5076): 1563-1565
2010, Vol. 23 
