Skip to main content

Selective Visual Attention System Based on Spatiotemporal Features

  • Conference paper
Computer-Human Interaction (APCHI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5068))

Included in the following conference series:

Abstract

In this paper, a selective visual attention system for motion pictures is proposed. The proposed attention system is new in that it utilizes motion information for the purpose of detecting Region Of Interest (ROI) or Focus Of Attention (FOA) in motion pictures. Typical feature integration model is expanded to incorporate motion stimulus in our suggested model. Suggested model is able to respond to motion stimulus by employing motion fields as one of temporal features to the feature integration model. Analysis of motion field maps and incorporation of the result are distinct from some of the previous studies on spatial feature integration. Comparative experiments with a human subjective evaluation show that correct detection rate of visual attention regions improves by utilizing temporal features compared to the case of using only spatial features.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Article  Google Scholar 

  2. Osberger, W., Maeder, A.J.: Automatic identification of Perceptually important regions in an image. In: Proc. of 14th Intl. Conf. On Pattern Recognition, vol. 1, pp. 701–704 (1998)

    Google Scholar 

  3. Clark, A.: Some Logical Features of Feature Integration. In: Inaugural lecture for the Italian Institute for Philosophical Studies, Intl School of Biophysics Study Program “From Neuronal Coding to Consciousness”, pp.12–17, Ischia, Naples (1998)

    Google Scholar 

  4. Clark, A.: Neuronal Coding of Perceptual Systems. Series on Biophysics and Biocybernetics, vol. 9, pp. 3–20. World Scientific, New Jersey (2001)

    Google Scholar 

  5. Anderson, R.A., Snyder, L.H., Bradley, D.C., Xing, J.: Multimodal Representation of Space in the Posterior Parietal Cortex and Its Use in Planning Movements. Annual Review Neuroscience 20, 303–330 (1997)

    Article  Google Scholar 

  6. Treisman, A.M., Gelade, G.: A Feature-integration Theory of Attention. Cognitive Psychology 12(1), 97–136 (1980)

    Article  Google Scholar 

  7. Cave, K.R.: The FeatureGate Model of Visual Selection. In: Psychological Research, vol. 62, pp. 182–194. Springer, Heidelberg (1999)

    Google Scholar 

  8. Cepeda, N.J., Cave, K.R., Bichot, N.P., Kim, M.S.: Spatial Selection via Feature-driven Inhibition of Distractor Locations. Perception and Psychophysics 60(5), 727–746 (1998)

    Google Scholar 

  9. Kim, M.S., Cave, K.R.: Top-down and Bottom-up Attentional Control: on the Nature of Interference from a Salient Distractor. Perception and Psychophysics 61(5), 1009–1023 (1999)

    Google Scholar 

  10. Milanese, R., Wechsler, H., Gil, S., Bost, J., Pun, T.: Integration of Bottom-up and Top-down Cues for Visual Attention Using Non-Linear Relaxation. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 781–785 (1994)

    Google Scholar 

  11. Cheoi, K.J., Lee, Y.: A Feature-driven Attention Module for an Active Vision System. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 583–590. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Park, M.C., Cheoi, K.J.: An Adaptive ROI Detection System for Spatiotemporal Features. Journal of the Korea Contents Association 6(1) (2006)

    Google Scholar 

  13. Hanazawa, A.: Visual Psychophysics (2): Neural Mechanisms of Visual Information Processing. Journal of Image Information and Television Engineers 58(2), 199–204 (2004)

    Google Scholar 

  14. Boynton, R.M.: Human Color Vision. Holt, Rinehart and Winston, New York (1979)

    Google Scholar 

  15. Hecht, E.: Optics. 2nd edn., Sec.5.7. Addison Wesley, Reading (1987)

    Google Scholar 

  16. Lee, T.W., Wachtler, T., Sejnowski, T.J.: Color Opponency is an Efficient Representation of Spectral Properties in Natural Scenes. Vision Research 42, 2095–2103 (2002)

    Article  Google Scholar 

  17. Hiroshi, A.: Visual Psychophysics (8): Visual Motion Perception and Motion Pictures. Journal of Image Information and Television Engineers 58(8), 1151–1156 (2004)

    Google Scholar 

  18. http://web.psych.ualberta.ca/~iwinship/vision/visualpathways.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Seongil Lee Hyunseung Choo Sungdo Ha In Chul Shin

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Park, MC., Cheoi, K. (2008). Selective Visual Attention System Based on Spatiotemporal Features. In: Lee, S., Choo, H., Ha, S., Shin, I.C. (eds) Computer-Human Interaction. APCHI 2008. Lecture Notes in Computer Science, vol 5068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70585-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70585-7_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70584-0

  • Online ISBN: 978-3-540-70585-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics