Skip to main content

Bayesian Modeling of Visual Attention

  • Conference paper
  • 2802 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7664)

Abstract

The mechanism in the brain that determines which part of the multitude of sensory data is currently of most interest is called selective attention. There are two kinds of attention cues, stimulus-driven bottom-up cues and goal-driven top-down cues determined by cognitive phenomena like knowledge, expectations, reward, and current goals. In this paper, we propose a Bayesian approach that explains the optimal integration of top-down cues and bottom-up cues. The top down cues include appearance feature, contexts, and locations of a target. The bottom up attention (saliency) is defined as the joint probability of the local feature and context at a location in the scene. The feature and context is organized in a pyramid structure. In this way, multiscale saliency is easily implemented. We demonstrate that the proposed visual saliency effectively predicts human gaze in free-viewing of natural scenes.

Keywords

  • Visual attention
  • Visual saliency
  • Bayesian modeling

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE T. Pattern Anal. 20, 1254–1259 (1998)

    CrossRef  Google Scholar 

  2. Frintrop, S., Rome, E., Christensen, H.I.: Computational Visual Attention Systems and Their Cognitive Foundations: A Survey. ACM Trans. Appl. Percept. 7 (2010)

    Google Scholar 

  3. Itti, L., Koch, C.: Computational Modelling of Visual Attention. Nat. Rev. Neurosci. 2, 194–203 (2001)

    CrossRef  Google Scholar 

  4. Theeuwes, J.: Top-down and Bottom-up Control of Visual Selection. Acta Psychol. 135, 77–99 (2010)

    CrossRef  Google Scholar 

  5. Borji, A., Itti, L.: State-of-the-Art in Visual Attention Modeling. IEEE Trans. Pattern Anal. Mach. Intell. (2012)

    Google Scholar 

  6. Itti, L., Koch, C.: A Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention. Vision Res. 40, 1489–1506 (2000)

    CrossRef  Google Scholar 

  7. Bruce, N.D., Tsotsos, J.K.: Saliency, Attention, and Visual Search: an Information Theoretic Approach. J. Vis. 9, 5, 1–24 (2009)

    Google Scholar 

  8. Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: A Bayesian Framework for Saliency Using Natural Statistics. J. Vis. 8, 32, 1–20 (2008)

    Google Scholar 

  9. Gao, D., Han, S., Vasconcelos, N.: Discriminant Saliency, the Detection of Suspicious Coincidences, and Applications to Visual Recognition. IEEE Trans. Pattern. Anal. Mach. Intell. 31, 989–1005 (2009)

    CrossRef  Google Scholar 

  10. Gao, D., Vasconcelos, N.: Decision-Theoretic Saliency: Computational Principles, Biological Plausibility, and Implications for Neurophysiology and Psychophysics. Neural Comput. 21, 239–271 (2009)

    CrossRef  MATH  Google Scholar 

  11. Itti, L., Baldi, P.: Bayesian Surprise Attracts Human Attention. Vision Res. 49, 1295–1306 (2009)

    CrossRef  Google Scholar 

  12. Torralba, A., Oliva, A., Castelhano, M.S., Henderson, J.M.: Contextual Guidance of Eye Movements and Attention in Real-World Scenes: the Role of Global Features in Object Search. Psychol. Rev. 113, 766–786 (2006)

    CrossRef  Google Scholar 

  13. Xu, J., Yang, Z., Tsien, J.Z.: Emergence of Visual Saliency from Natural Scenes via Context-Mediated Probability Distributions Coding. PLoS One 5 (2010)

    Google Scholar 

  14. Galleguillos, C., Belongie, S.: Context Based Object Categorization: A Critical Survey. Computer Vision and Image Understanding 114, 712–722 (2010)

    CrossRef  Google Scholar 

  15. Olmos, A., Kingdom, F.A.: McGill Calibrated Color Image Database (2004)

    Google Scholar 

  16. Hyvarinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)

    CrossRef  Google Scholar 

  17. Bell, A.J., Sejnowski, T.J.: The “Independent Components” of Natural Scenes Are Edge Filters. Vision Res. 37, 3327–3338 (1997)

    CrossRef  Google Scholar 

  18. 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)

    CrossRef  Google Scholar 

  19. Simoncelli, E.P., Olshausen, B.A.: Natural Image Statistics and Neural Representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001)

    CrossRef  Google Scholar 

  20. Tatler, B.W., Baddeley, R.J., Gilchrist, I.D.: Visual Correlates of Fixation Selection: Effects of Scale and Time. Vision Res. 45, 643–659 (2005)

    CrossRef  Google Scholar 

  21. Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. In: Advances in Neural Information Processing Systems, pp. 155–162 (2006)

    Google Scholar 

  22. Gao, D., Vasconcelos, N.: Bottom-up Saliency Is a Discriminant Process. In: ICCV, Rio de Janeiro, Brazil (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, J. (2012). Bayesian Modeling of Visual Attention. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34481-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34480-0

  • Online ISBN: 978-3-642-34481-7

  • eBook Packages: Computer ScienceComputer Science (R0)