“What” and “Where” Information Based Attention Guidance Model

  • Mei Tian
  • Siwei Luo
  • Lingzhi Liao
  • Lianwei Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Visual system can be defined as consisting of two pathways. The classic definition labeled a “what” pathway to process object information and a “where” pathway to process spatial information. In this paper, we propose a novel attention guidance model based on “what” and “where” information. Context-centered “where” information is used to control top-down attention, and guide bottom-up attention which is driven by “what” information. The procedure of top-down attention can be divided into two stages: pre-attention and focus attention. In the stage of pre-attention, “where” information can be used to provide prior knowledge of presence or absence of objects which decides whether search operation is followed. By integrating the result of focus attention with “what” information, attention is directed to the region that is most likely to contain the object and series of salient regions are detected. Results of experiment on natural images demonstrate its effectiveness.


Visual Attention Gaussian Mixture Model Focus Attention Independent Component Analysis Salient Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Creem, S.H., Proffitt, D.R.: Defing the Cortical Cisual Systems: “What”, “Where”, and “How”. Acta Psychologica. 107, 43–68 (2001)CrossRefGoogle Scholar
  2. 2.
    Marr, D.: Vision: a Computational Investigation into the Human Representation and Processing of Visual Information. Freeman, W. H, San Francisco (1982)Google Scholar
  3. 3.
    Itti, L., Gold, C., Koch, C.: Visual Attention and Target Detection in Cluttered Natural Scenes. Optical Engineering 40, 1784–1793 (2001)CrossRefGoogle Scholar
  4. 4.
    Zhang, P., Wang, R.S.: Detecting Salient Regions Based on Location Shift and Extent Trace. Journal of Software 15, 891–898 (2004)MATHGoogle Scholar
  5. 5.
    Frintrop, S., Rome, E.: Simulating Visual Attention for Object Recognition. In: Proceedings of the Workshop on Early Cognitive Vision (2004)Google Scholar
  6. 6.
    Sun, Y., Fisher, R.: Object-Based Visual Attention for Computer Vision. Artificial Intelligence 146, 77–123 (2003)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Ouerhani, N.: Visual Attention: from Bio-Inspired Modeling to Real-Time Implementation. Institute of Micro technology, Switzerland (2003)Google Scholar
  8. 8.
    Long, F.H., Zheng, N.N.: A Visual Computing Model Based on Attention Mechanism. Journal of Image and Graphics 3, 592–595 (1998)Google Scholar
  9. 9.
    Rybak, I.A., Gusakova, V.I., Golovan, A.V., Podladchikova, L.N., Shevtsova, N.A.: A Model of Attention-Guided Visual Perception and Recognition. Vision Research 38, 2387–2400 (1998)CrossRefGoogle Scholar
  10. 10.
    Salah, A.A., Alpaydin, E., Akarun, L.: A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 420–425 (2002)CrossRefGoogle Scholar
  11. 11.
    Itti, L.: Models of Bottom-Up and Top-Down Visual Attention. California Institute of Technology, Pasadena (2000)Google Scholar
  12. 12.
    Navalpakkam, V., Itti, L.: A Goal Oriented Attention Guidance Model. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 453–461. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Itti, L., Koch, C.: Computational Modeling of Visual Attention. Nature Reviews Neuroscience 2, 194–230 (2001)CrossRefGoogle Scholar
  14. 14.
    Henderson, J.M.: Human Gaze Control during Real-World Scene Perception. Trends in Cognitive Sciences 7, 498–504 (2003)CrossRefGoogle Scholar
  15. 15.
    Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Network 10, 626–634 (1999)CrossRefGoogle Scholar
  16. 16.
    Itti, L., Koch, C.: Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging 10, 161–169 (2001)CrossRefGoogle Scholar
  17. 17.
    Lee, T.W., Lewicki, M.S.: The Generalized Gaussian Mixture Model Using ICA. In: International Workshop on Independent Component Analysis (ICA 2000), pp. 239–244 (2000)Google Scholar
  18. 18.
    Liu, Y.H., Luo, S.W., Li, A.J., Yu, H.B.: A New Model Selection Criterion Based on Information Geometry. In: 7th Intern. Conf. on Signal Processing (ICSP 2004), vol. 2, pp. 1562–1565 (2004)Google Scholar
  19. 19.
    Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report, ICSI-TR-97-021, University of Berkeley, Berkeley (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mei Tian
    • 1
  • Siwei Luo
    • 1
  • Lingzhi Liao
    • 1
  • Lianwei Zhao
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

Personalised recommendations