Attentional Selection for Object Recognition — A Gentle Way

  • Dirk Walther
  • Laurent Itti
  • Maximilian Riesenhuber
  • Tomaso Poggio
  • Christof Koch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)


Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathway from the attended region of the retinal input to the recognition units. However, there is little physiological evidence for such all-or-none modulation in early areas. We present a combined model for spatial attention and object recognition in which the recognition system monitors the entire visual field, but attentional modulation by as little as 20% at a high level is sufficient to recognize multiple objects. To determine the size and shape of the region to be modulated, a rough segmentation is performed, based on pre-attentive features already computed to guide attention. Testing with synthetic and natural stimuli demonstrates that our new approach to attentional selection for recognition yields encouraging results in addition to being biologically plausible.


Object Recognition Recognition System Spatial Attention Attention System Attentional Modulation 
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.


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  1. 1.
    C. Koch and S. Ullman. Shifts in selectiv visual-attention—towards the underlying neural circuitry. Hum. Neurobiol. 4(4):219–227,1985.Google Scholar
  2. 2.
    L. Itti and C. Koch. Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3):194–203, 2001.CrossRefGoogle Scholar
  3. 3.
    F. Miau and L. Itti. A neural model combining attentional orienting to object recognition:Preliminary explorations on the interplay between where and what. In IEEE Engin in Medicine and Biology Society (EMBS), Istanbul,Turkey, 2001.Google Scholar
  4. 4.
    B.A. Olshausen, C.H. Anderson, and D.C. Van Essen. A neurobiological model of visual-attention and invariant pattern-recognition based on dynamic routing of information. J. Neurosci.13(11):4700–4719, 1993.Google Scholar
  5. 5.
    J.K. Tsotsos, S.M. Culhane, W.Y.K. Wai, Y.H. Lai, N. Davis, and F. Nuflo. Modeling visual-attention via selective tuning.Artif. Intell.78:507–545, 1995.CrossRefGoogle Scholar
  6. 6.
    J.H. Reynolds, T. Pasternak, and R. Desimone. Attention increases sensitivity of V4 neurons. Neuron 26(3):703–714, 2000.CrossRefGoogle Scholar
  7. 7.
    D. Walther, M. Riesenhuber, T. Poggio, L. Itti, and C. Koch. Towards an integrated model of saliency-based attention and object recognition in the primate’ s visual system. J. Cogn. Neurosci. B14 Suppl. S:46–47, 2002.Google Scholar
  8. 8.
    L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE PAMI, 20(11): 1254–1259, 1998.CrossRefGoogle Scholar
  9. 9.
    L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res.40(10–12):1489–1506, 2000.CrossRefGoogle Scholar
  10. 10.
    M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nat.Neurosci.2(11):1019–1025, 1999.CrossRefGoogle Scholar
  11. 11.
    M. Riesenhuber and T. Poggio. Are cortical models really bound by the “binding problem ”? Neuron 24(1):87–93, 111-25, 1999.CrossRefGoogle Scholar
  12. 12.
    K. Fukushima. Neocognitron:A self-organizing neural network model for a mechan. of pattern recogn. unaffected by shifts in position. Biol. Cybern.36:193–202, 1980.MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    S. Treue. Neural correlates of attention in primate visual cortex. Trends Neurosci. 24(5):295–300, 2001.CrossRefGoogle Scholar
  14. 14.
    C.E. Connor, D.C. Preddie, J.L. Gallant, and D.C. Van Essen. Spatial attention effects in macaque area V4. J.Neurosci.17(9):3201–3214, 1997.Google Scholar
  15. 15.
    B.C. Motter. Neural correlates of attentive selection for color or luminance in extrastriate area V4. J.Neurosci.14(4):2178–2189, 1994.Google Scholar
  16. 16.
    S.J. Luck, L. Chelazzi, S.A. Hillyard, and R. Desimone. Neural mechanisms of spatial selective attention in areas V1,V2,and V4 of macaque visual cortex. J. Neurophysiol.77(1):24–42, 1997.Google Scholar
  17. 17.
    J. Intriligator and P. Cavanagh. The spatial resolution of visual attention.Cogn. Psychol.43(3):171–216, 2001.CrossRefGoogle Scholar
  18. 18.
    J. Braun. Visual-search among items of different salience removal of visual-at-tention mimics a lesion in extrastriate area V4. J. Neurosci.14(2):554–567, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Dirk Walther
    • 1
  • Laurent Itti
    • 2
  • Maximilian Riesenhuber
    • 3
  • Tomaso Poggio
    • 3
  • Christof Koch
    • 1
  1. 1.Computation and Neural Systems ProgramCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Center for Biological and Computational LearningMassachusetts Institute of TechnologyCambridgeUSA

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