Localization of Attended Multi-feature Stimuli: Tracing Back Feed-Forward Activation Using Localized Saliency Computations

  • John K. Tsotsos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


This paper demonstrates how attended stimuli may be localized even if they are complex items composed of elements from several different feature maps and from different locations within the Selective Tuning (ST) model. As such, this provides a step towards the solution of the ‘binding problem’ in vision. The solution relies on a region-based winner-take-all algorithm, a definition of a featural receptive field for neurons where several representations provide input from different spatial areas, and a localized, distributed saliency computation specialized for each featural receptive field depending on its inputs. A top-down attentive mechanism traces back the connections activated by feed-forward stimuli to localize and bind features into coherent wholes.


Filter Bank Winning Region Pass Zone Binding Problem Selective Tuning 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tsotsos, J.K.: A Complexity Level Analysis of Vision. Behavioral and Brain Sciences 13, 423–455 (1990)Google Scholar
  2. 2.
    Tsotsos, J.K., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 8, 1–2, 507–547 (1995)Google Scholar
  3. 3.
    Rosenblatt, F.: Principles of Neurodynamics: Perceptions and the Theory of Brain Mechanisms. Spartan Books (1961)Google Scholar
  4. 4.
    Tsotsos, J.K.: The Complexity of Perceptual Search Tasks. In: Proc. International Joint Conference on Artificial Intelligence Detroit, pp. 1571–1577 (1989)Google Scholar
  5. 5.
    Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12, 97–136 (1980)CrossRefGoogle Scholar
  6. 6.
    Felleman, D., Van Essen, D.: Distributed Hierarchical Processing in the Primate Visual Cortex. Cerebral Cortex 1, 1–47 (1991)CrossRefGoogle Scholar
  7. 7.
    von der Malsburg, C.: The correlation theory of brain function, Internal Rpt. 81-2, Dept. of Neurobiology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany (1981)Google Scholar
  8. 8.
    Gray, C.M.: The Temporal Correlation Hypothesis of Visual Feature Integration, Still Alive and Well. Neuron 24(1), 31–47 (1999)CrossRefGoogle Scholar
  9. 9.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annual Reviews of Neuroscience 18, 193–222 (1995)CrossRefGoogle Scholar
  10. 10.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2, 194–204 (2001)CrossRefGoogle Scholar
  11. 11.
    Riesenhuber, M., Poggio, T.: Are Cortical Models Really Bound by the “Binding Problem”? Neuron 24(1), 87–93 (1999)CrossRefGoogle Scholar
  12. 12.
    Mehta, A.D., Ulbert, I., Schroeder, C.E.: Intermodal Selective Attention in Monkeys. I: Distribution and Timing of Effects across Visual Areas. Cerebral Cortex 10(4), 343–358 (2000)CrossRefGoogle Scholar
  13. 13.
    Connor, D.O., Fukui, M., Pinsk, M., Kastner, S.: Attention modulates responses in the human lateral geniculate nucleus. Nature Neuroscience 5(11), 1203–1209 (2002)CrossRefGoogle Scholar
  14. 14.
    Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J., Pomplun, M., Simine, E., Zhou, K.: Attending to Motion. Computer Vision and Image Understanding 100(1-2), 3–40 (2005)CrossRefGoogle Scholar
  15. 15.
    Tsotsos, J.K., Culhane, S., Cutzu, F.: From Theoretical Foundations to a Hierarchical Circuit for Selective Attention. In: Braun, J., Koch, C., Davis, J. (eds.) Visual Attention and Cortical Circuits, pp. 285–306. MIT Press, Cambridge (2001)Google Scholar
  16. 16.
    Tsotsos, J.K.: An Inhibitory Beam for Attentional Selection. In: Harris, L., Jenkin, M. (eds.) Spatial Vision in Humans and Robots, pp. 313–331. Cambridge University Press, Cambridge (1993); (papers from York University International Conference on Vision, June 1991, Toronto)Google Scholar
  17. 17.
    Fukushima, K.: A neural network model for selective attention in visual pattern recognition. Biological Cybernetics 55(1), 5–15 (1986)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John K. Tsotsos
    • 1
  1. 1.Dept. of Computer Science & Engineering, and, Centre for Vision ResearchYork UniversityTorontoCanada

Personalised recommendations