Skip to main content
SpringerLink
Log in

Guiding Attention by Cooperative Cues

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

A common assumption in visual attention is based on the rationale of “limited capacity of information processing”. From this view point there is little consideration of how different information channels or modules are cooperating because cells in processing stages are forced to compete for the limited resource. To examine the mechanism behind the cooperative behavior of information channels, a computational model of selective attention is implemented based on two hypotheses. Unlike the traditional view of visual attention, the cooperative behavior is assumed to be a dynamic integration process between the bottom-up and top-down information. Furthermore, top-down information is assumed to provide a contextual cue during selection process and to guide the attentional allocation among many bottom-up candidates. The result from a series of simulation with still and video images showed some interesting properties that could not be explained by the competitive aspect of selective attention alone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Humphreys G W, Bruce V. Visual Cognition: Computational, Experimental and Neuropsychological Perspectives. East Sussex: Lawrence Erlbaum Associates Ltd., UK, 1987.

  2. Pashler H E. The Psychology of Attention. Cambridge, Mass.: MIT Press, 1998.

    Google Scholar 

  3. Broadbent D E. Perception and Communication. London: Pergamon, 1958.

    Google Scholar 

  4. Desimone R, Duncan J. Neural mechanism of selective attention. Annual Review of Neuroscience, 1995, 18: 193–222.

    Article  Google Scholar 

  5. Kastner S, DeWeerd P, Desimone R et al. Mechanisms of directed attention in ventral extrastriate cortex as revealed by functional MRI. Science, 1998, 282(5386): 108–111.

    Article  Google Scholar 

  6. Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254–1259.

    Article  Google Scholar 

  7. Itti L, Koch C. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 2000, 40(10–12): 1489–1506.

    Article  Google Scholar 

  8. Sun Y, Fisher R. Hierarchical selectivity for object-based visual attention. In Proc. 2nd Workshop on Biologically Motivated Computer Vision (BMCV'02), Tuebingen, Germany, 2002, pp.427–438.

  9. Deco G, Schurmann B. A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision Research, 2000, 40(20): 2845–2859.

    Article  Google Scholar 

  10. Carota L, Indiverib A G, Dantec V. A software-hardware selective attention system. Neurocomputing, 2004, 58–60: 647–653.

    Article  Google Scholar 

  11. Standage D I, Trappenberg T P, Klein R M. Modelling divided visual attention with a winner-take-all network. Neural Networks, 2005, 18(5/6): 620–627.

    Article  Google Scholar 

  12. Wolfe J M, Gancarz G. Guided Search 3.0: A Model of Visual Search Catches Up with Jay Enoch 40 Years Later. Basic and Clinical Applications of Vision Science, Lakshminarayanan V (ed.), Dordrecht: Kluwer Academic Press, Netherlands, 1996, pp.1989–1992.

  13. Sun Y, Fisher R. Object-based visual attention for computer vision. Artificial Intelligence, 2003, 146(1): 77–123.

    Article  MATH  MathSciNet  Google Scholar 

  14. Rainer G, Assad W F, Miller E K. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature, 1999, 393: 577–579.

    Google Scholar 

  15. Spratling M W. Cortical region interactions and the functional role of apical dendrites. Behavioral and Cognitive Neuroscience Reviews, 2002, 1(3): 219–228.

    Article  Google Scholar 

  16. Treue S, Martinez-Trujillo J C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature, 1999, (6736): 575–579.

    Article  Google Scholar 

  17. Reynolds J H, Pasternak T, Desimone R. Attention increases sensitivity of v4 neurons. Neuron, 2000, 26(3): 703–714.

    Article  Google Scholar 

  18. Posner M I. Orienting of attention. Quarterly Journal of Experimental Psychology, 1980, 32: 3–25.

    Article  Google Scholar 

  19. Posner M I, Snyder C R R, Davidson B J. Attention and the detection of signals. Journal of Experimental Psychology: General, 1980, 109: 160–174.

    Article  Google Scholar 

  20. Lee KW, Feng J, Buxton H. Selective attention for cue-guided search using a spiking network. In Proc. International Workshop on Attention and Performance in Computer Vision (WAPCV'03), Graz, Austria, 2003, pp.27–33.

  21. Lee KW. Computational model of selective attention: Integrative approach [Dissertation]. University of Sussex, 2004.

  22. Lee KW, Feng J, Buxton H. Cue-guided search: A computational model of selective attention. IEEE Transactions on Neural Networks, 2005, 16(4): 910–924.

    Article  Google Scholar 

  23. Koch C. Biophysics of Computation: Information Processing in Single Neurons. New York: Oxford University Press, 1999.

    Google Scholar 

  24. Vezhnevets V, Sazonov V, Andreeva A. A survey on pixel-based skin color detection techniques. In Proc. Graphicon-2003, Moscow, Russia, 2003, pp.85–92.

  25. Govindaraju V. Locating human faces in photographs. International Journal of Computer Vision, 1996, 19(2): 129–146.

    Article  MathSciNet  Google Scholar 

  26. Reisfeld D, Wolfson H, Yeshurun Y. Context free attentional operator: The generalized symmetry transform. International Journal of Computer Vision, 1995, 14: 119–130.

    Article  Google Scholar 

  27. Kovesi P. Symmetry and asymmetry from local phase. In Proc. Tenth Australian Joint Conference on Artificial Intelligence (AI'97), Perth, Australia, 1997, pp.185–190.

  28. Kim H-S, Kang W-S, Shin J-I, Park S-H. Face detection using template matching and ellipse fitting. IEICE Transaction on Information and System, 2000, E38-D(11): 2008–2011.

    Google Scholar 

  29. Sirohey S A. Human face segmentation and identification. Technical Report CS-TR-3176, University of Maryland, 1993.

  30. Borovicka J. Circle detection using hough transforms documentation. http://linux.fj.cvut.cz/∼pinus/bristol/imageproc/hw1/report.pdf, 2003.

Download references

Author information

Authors and Affiliations

  1. School of Media, Soongsil University, 1-1, Sangdo-5 Dong, Dong-Jak Ku, Seoul, 156-743, Korea

    KangWoo Lee

Authors
  1. KangWoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to KangWoo Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, K. Guiding Attention by Cooperative Cues. J. Comput. Sci. Technol. 23, 874–884 (2008). https://doi.org/10.1007/s11390-008-9171-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-008-9171-6

Keywords

Search

Navigation