Abstract Based on the previous attention selection model with visual memory as top-down guidance, a visual memory model is put forward with online learning and forgetting, called amnesic incremental hierarchical discriminant regression (AIHDR) tree, to mimic human short-term memory (STM) and long-term memory (LTM). A self-supervised competition neural network (SSCNN) combines the information from both bottom-up and top-down to find out the focus of attention (FoA). The connection weights in SSCNN can be updated in real-time according to the environment. Experimental results show that the proposed model can mimic the shift of human attention and stare at an interesting object consciously when environment changes.
[1] Treisman A M, Gelade G. A Feature-Integration Theory of Attention. Cognitive Psychology, 1980, 12(1): 97-136 [2] Wolfe J M. Guided Search 2.0: A Revised Model of Visual Search. Psychonomic Bulletin & Review, 1994, 1(2): 202-238 [3] Koch C, Ullman S. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology, 1985, 4(4): 219-227 [4] 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 [5] Lee K W, Buxton H, Feng Jianfeng. Cue-Guided Search: A Computational Model of Selective Attention. IEEE Trans on Neural Networks, 2005, 16(4): 910- 924 [6] Downing P E. Interactions between Visual Working Memory and Selective Attention. Psychological Science, 2000, 11(6): 467-473 [7] Moores E, Laiti L, Chelazzi L. Associative Knowledge Controls Deployment of Visual Selective Attention. Nature Neuroscience, 2003, 6(2): 182-189 [8] Guo Chenlei, Zhang Liming. Attention Selection with Self-Supervised Competition Neural Network and Its Applications in Robot // International Symposium on Neural Networks. Nanjing, China, 2007: 722-732 [9] Weng Juyang, Hwang W S. Incremental Hierarchical Discriminant Regression. IEEE Trans on Neural Networks, 2007, 18(2): 397-415 [10] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110 [11] Walther D, Rutishauser U, Koch C, et al. Selective Visual Attention Enables Learning and Recognition of Multiple Objects in Cluttered Scenes. Computer Vision and Image Understanding, 2005, 100(1/2): 41-63 [12] Gold J M, Murray R F, Sekuler A B, et al. Visual Memory Decay Is Deterministic. Psychological Science, 2005, 16(10): 769-774
2008, Vol. 21 
