Advertisement

Selective Attention in the Learning of Viewpoint and Position Invariance

  • Muhua Li
  • James J. Clark
Conference paper
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

Selective attention plays an important role in visual processing in reducing the problem scale and in actively gathering useful information. We propose a modified saliency map mechanism that uses a simple top-down task-dependent cue to allow attention to stay mainly on one object in the scene each time for the first few shifts. Such a method allows the learning of invariant object representations across attention shifts in a multiple-object scene. In this paper, we construct a neural network that can learn position and viewpoint invariant representations for objects across attention shifts in a temporal sequence.

Keywords

Body Motion Local Feature Selective Attention Sparse Code Attention Shift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bülthoff, H.H., Wallraven, C., Graf, A.B.A.: View-based dynamic object recognition based on human perception. In: Proceedings of 16th International Conference on pattern recognition, vol. 3, pp. 768–776 (2002)Google Scholar
  2. 2.
    Carrasco, M., Chang, I.: The interaction of objective and subjective organizations in a localization search task. Perception and Psychophysics 57(8), 1134–1150 (1995)CrossRefGoogle Scholar
  3. 3.
    Clark, J.J., O’Regan, J.K.: A Temporal-difference learning model for perceptual stability in color vision. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 503–506 (2000)Google Scholar
  4. 4.
    Földiák, P.: Learning invariance from transformation sequences. Neural Computation 3, 194–200 (1991)CrossRefGoogle Scholar
  5. 5.
    Hafed, Z.M.: Motor theories of attention: How action serves perception in the visual system. Ph.D Thesis, McGill University, Canada (2003)Google Scholar
  6. 6.
    Heinke, D., Humphreys, G.W.: Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review 110(1), 29–87 (2003)CrossRefGoogle Scholar
  7. 7.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  8. 8.
    Koch, C., Ullman, S.: Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)Google Scholar
  9. 9.
    Li, M., Clark, J.J.: A temporal stability approach to position and attention shift invariant recognition. Neural Computation 16(11), 2293–2321 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Li, M., Clark, J.J.: Learning of position-invariant object representation across attention shifts. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G.W. (eds.) WAPCV 2004. LNCS, vol. 3368, pp. 57–70. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Logothetis, N.K., Pauls, J., Bülthoff, H., Poggio, T.: View-dependent object recognition by monkeys. Current Biology 4(5), 401–414 (1994)CrossRefGoogle Scholar
  12. 12.
    Logothetis, N.K., Pauls, J., Poggio, T.: Shape representation in the inferior temporal cortex of monkeys. Current Biology 5(5), 552–563 (1995)CrossRefGoogle Scholar
  13. 13.
    Maunsell, J.H.R., Cook, E.P.: The role of attention in visual processing. Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences 357(1424), 1063–1072 (2002)CrossRefGoogle Scholar
  14. 14.
    Olshausen, B.A., Anderson, C.H., Van Essen, D.C.: A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. The Journal of Neuroscience 13(11), 4700–4719 (1993)Google Scholar
  15. 15.
    Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37, 3311–3325 (1997)CrossRefGoogle Scholar
  16. 16.
    Rumelhart, D.I., Zipser, D.: A complex-cell receptive-filed model. Journal of Neurophysiology 53, 1266–1286 (1985)Google Scholar
  17. 17.
    Tarr, M.: Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects. Psychonomic Bulletin and Review 2, 55–82 (1995)CrossRefGoogle Scholar
  18. 18.
    Tarr, M., Williams, P., Hayward, W., Gauthier, I.: Three-dimensional object recognition is viewpoint-dependent. Nature Neuroscience 1(4), 275–277 (1998)CrossRefGoogle Scholar
  19. 19.
    Wallis, G., Bülthoff, H.H.: Effect of temporal association on recognition memory. Proceedings of the National Academy of Science 98, 4800–4804 (2001)CrossRefGoogle Scholar
  20. 20.
    Wallis, G., Rolls, E.T.: Invariant face and object recognition in the visual system. Progress in Neurobiology 51, 167–194 (1997)CrossRefGoogle Scholar
  21. 21.
    Wolfe, J.M., O’Neill, P.: Why are there Eccentricity Effects in Visual Search? Perception and Psychophysics 60(1), 140–156 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhua Li
    • 1
  • James J. Clark
    • 1
  1. 1.Centre for Intelligent Machines, McGill UniversityCanada

Personalised recommendations