Advertisement

An Information Theoretic Model of Saliency and Visual Search

  • Neil D. B. Bruce
  • John K. Tsotsos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

In this paper, a proposal which quantifies visual saliency based on an information theoretic definition is evaluated with respect to visual psychophysics paradigms. Analysis reveals that the proposal explains a broad range of results from classic visual search tasks, including many for which only specialized models have had success. As a whole, the results provide strong behavioral support for a model of visual saliency based on information, supplementing earlier work revealing the efficacy of the approach in predicting primate fixation data.

Keywords

Attention Visual Search Saliency Information Theory Fixation Entropy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bruce, N., Tsotsos, J.K.: Saliency Based on Information Maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)Google Scholar
  2. 2.
    Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRefGoogle Scholar
  3. 3.
    Bell, A.J., Sejnowski, T.J.: The ‘Independent Components’ of Natural Scenes are Edge Filters. Vision Research 37(23), 3327–3338 (1997)CrossRefGoogle Scholar
  4. 4.
    Cardoso, J.F.: High-order contrasts for independent component analysis. Neural Computation 11(1), 157–192 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Itti, L., Baldi, P.: Bayesian Surprise Attracts Human Attention. Advances in Neural Information Processing Systems 18, 547–554 (2006)Google Scholar
  6. 6.
    Li, Z.: A saliency map in primary visual cortex. Trends in Cognitive Sciences 6(1), 9–16 (2002)CrossRefGoogle Scholar
  7. 7.
    Treisman, A., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12, 97–136 (1980)CrossRefGoogle Scholar
  8. 8.
    Wolfe, J.M.: What Can 1,000,000 Trials Tell Us About Visual Search? Psychological Science 9(1) (1998)Google Scholar
  9. 9.
    Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychol. Rev. 433, 433–458 (1989)CrossRefGoogle Scholar
  10. 10.
    Pashler, H.: Target-distractor discriminability in visual search. Perception & Psychophysics 41, 285–292 (1987)CrossRefGoogle Scholar
  11. 11.
    Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5, 1–7 (2004)CrossRefGoogle Scholar
  12. 12.
    Tombu, M.N., Tsotsos, J.K.: Attentional inhibitory surrounds in orentation space. Journal of Vision 5(8), 1013, 1013a (2005)Google Scholar
  13. 13.
    Tsotsos, J.K., Culhane, S., Yan Kei Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial intelligence 78, 507–545 (1995)CrossRefGoogle Scholar
  14. 14.
    Rosenholtz, R., Nagy, A.L., Bell, A.R.: The effect of background color on asymmetries in color search. Journal of Vision 4(3), Article 9, 224–240 (2004)Google Scholar
  15. 15.
    Treisman, A., Gormican, S.: Feature analysis in early vision: evidence from search asymmetries. Psychol Rev. 95(1), 15–48 (1988)CrossRefGoogle Scholar
  16. 16.
    Rosenholtz, R.: Search asymmetries? What search asymmetries? Perception & Psychophysics 63(3), 476–489 (2001)CrossRefGoogle Scholar
  17. 17.
    Schwartz, O., Simoncelli, E.: Natural signal statistics and sensory gain control. Nature Neuroscience 4(8), 819–825 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Neil D. B. Bruce
    • 1
  • John K. Tsotsos
    • 1
  1. 1.Department of Computer Science and Engineering and, Centre for Vision Research, York University, Toronto, ONCanada

Personalised recommendations