Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention

  • Muhammad Zaheer Aziz
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)


This paper proposes a novel approach to construct saliency map of color contrast and an enhanced technique for inhibition of return on this map for artificial visual attention. The ability to handle dynamic scenes is also included in the model by introducing a memory based mechanism. For the process of color map construction the traditionally followed concept of double-opponent colors is extended by implementing the concepts of contrast from the subject of color theory. In context of inhibition of return, the color based inhibition is also modeled according to recent research in human vision apart from the commonly implemented spatial inhibition. The proposed methods have produced results compatible with the existing models of visual attention whereas the region-based nature of the proposed technique renders advantages of precise localization of the foci of attention, proper representation of the shapes of the attended objects, and accelerated computation time.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Law, M.B., Pratt, J., Abrams, R.A.: Color-based inhibition of return. Perception & Psychophysics, 402–408 (1995)Google Scholar
  2. 2.
    Itti, L., Koch, C.: A saliency based search mechanism for overt and covert shifts of visual attention. Vision Research, 1489–1506 (2000)Google Scholar
  3. 3.
    Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: IEEE International Conference on Computer Vision and Pattern Recognition, Washington DC, pp. 37–44 (2004)Google Scholar
  4. 4.
    Engel, S., Zhang, X., Wandell, B.: Color tuning in human visual cortex measured with functional magnetic resonance imaging. Nature 388, 68–71 (1997)CrossRefGoogle Scholar
  5. 5.
    Aziz, M.Z., Mertsching, B.: Color segmentation for a region-based attention model. In: Farbworkshop 2006, Ilmenau - Germany, pp. 74–83 (2006)Google Scholar
  6. 6.
    Aziz, M.Z., Mertsching, B., Shafik, M.S., Stemmer, R.: Evaluation of visual attention models for robots. In: ICVS 2006, IEEE, New York - USA (2006)Google Scholar
  7. 7.
    Aziz, M.Z., Stemmer, R., Mertsching, B.: Region-based depth feature map for visual attention in autonomous mobile systems. In: AMS 2005, Stuttgart - Germany, Informatik Aktuell, pp. 89–95. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Aziz, M.Z., Mertsching, B.: Pop-out and IOR in static scenes with region based visual attention. In: WCAA-ICVS 2007, Bielefeld University eCollections, Bielefeld - Germany (2007)Google Scholar
  9. 9.
    Aziz, M.Z., Mertsching, B.: Color saliency and inhibition in region based visual attention. In: WAPCV 2007, Hyderabad - India, pp. 95–108 (2007)Google Scholar
  10. 10.
    Itti, L., Koch, U., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)CrossRefGoogle Scholar
  11. 11.
    Sun, Y., Fischer, R.: Object-based visual attention for computer vision. Artificial Intelligence 146, 77–123 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Park, S.J., Ban, S.J., Sang, S.W., Shin, J.K., Lee, M.: Implementation of visual attention system using bottom-up saliency map model. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 678–685. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Meur, O.L., Callet, P.L., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. Transactions on Pattern Analysis and Machine Intelligence 28, 802–817 (2006)CrossRefGoogle Scholar
  14. 14.
    Stentiford, F.: An estimator for visual attention through competitive novelty with application to image compression. In: Picture Coding Symposium, Seoul - Korea, pp. 101–104 (2001)Google Scholar
  15. 15.
    Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. Transactions on Pattern Analysis and Machine Intelligence 23, 1415–1429 (2001)CrossRefGoogle Scholar
  16. 16.
    Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Dankers, A., Barnes, N., Zelinsky, A.: A reactive vision system: Active-dynamic saliency. In: ICVS 2007, Bielefeld University, Bielefeld, Germany (2007)Google Scholar
  18. 18.
    Atsumi, M.: Stochastic attentional selection and shift on the visual attention pyramid. In: ICVS 2007, Bielefeld - Germany (2007)Google Scholar
  19. 19.
    Ford, J.L.: Internet Resource (2006),
  20. 20.
    Itten, J.: The Elements of Color. John Wiley & Sons Inc., New York, USA (1961)Google Scholar
  21. 21.
    Mahnke, F.: Color, Environment, and Human Response. Van Nostrand Reinhold, Detroit (1996)Google Scholar
  22. 22.
    Goolsby, B.A., Grabowecky, M., Suzuki, S.: Adaptive modulation of color salience contingent upon global form coding and task relevance. Vision Research, 901–930 (2005)Google Scholar
  23. 23.
    Cutzua, F., Tsotsos, J.K.: The selective tuning model of attention: Psychophysical evidence for a suppressive annulus around an attended item. Vision Research, 205–219 (2003)Google Scholar
  24. 24.
    Aziz, M.Z., Mertsching, B.: An attentional approach for perceptual grouping of spatially distributed patterns. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 345–354. Springer, Heidelberg (2007)Google Scholar
  25. 25.
    Frintrop, S., Klodt, M., Rome, E.: A real-time visual attention system using integral images. In: ICVS 2007, Bielefeld University eCollections, Bielefeld - Germany (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhammad Zaheer Aziz
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LAB, Universität Paderborn, 33098 PaderbornGermany

Personalised recommendations