Saliency Based on Decorrelation and Distinctiveness of Local Responses

  • Antón Garcia-Diaz
  • Xosé R. Fdez-Vidal
  • Xosé M. Pardo
  • Raquel Dosil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)


In this paper we validate a new model of bottom-up saliency based in the decorrelation and the distinctiveness of local responses. The model is simple and light, and is based on biologically plausible mechanisms. Decorrelation is achieved by applying principal components analysis over a set of multiscale low level features. Distinctiveness is measured using the Hotelling’s T2 statistic. The presented approach provides a suitable framework for the incorporation of top-down processes like contextual priors, but also learning and recognition. We show its capability of reproducing human fixations on an open access image dataset and we compare it with other recently proposed models of the state of the art.


saliency bottom-up attention eye-fixations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tsotsos, J.K.: Computational foundations for attentive Processes. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 3–7. Elsevier, Amsterdam (2005)CrossRefGoogle Scholar
  2. 2.
    Frintrop, S., Jensfelt, P.: Attentional Landmarks and Active Gaze Control for Visual SLAM. IEEE Transactions on Robotics, Special Issue on Visual SLAM 24(5) (2008)Google Scholar
  3. 3.
    Harel, J., Koch, C.: On the Optimality of Spatial Attention for Object Detection, Attention in Cognitive Systems. In: WAPCV (2008)Google Scholar
  4. 4.
    Bruce, N., Tsotsos, J.K.: Saliency Based on Information Maximization. In: NIPS, vol. 18, pp. 155–162 (2006)Google Scholar
  5. 5.
    Bruce, N., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24 (2009)CrossRefGoogle Scholar
  6. 6.
    Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8(7), 13, 1–18 (2008)CrossRefGoogle Scholar
  7. 7.
    Gao, D., Mahadevan, V., Vasconcelos, N.: The discriminant center-surround hypothesis for bottom-up saliency. In: NIPS (2007)Google Scholar
  8. 8.
    Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: NIPS, vol. 19, pp. 545–552 (2007)Google Scholar
  9. 9.
    Olshausen, B.A., Field, D.J.: How Close Are We to Understanding V1? Neural Computation 17, 1665–1699 (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 802–817 (2006)CrossRefGoogle Scholar
  11. 11.
    Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R.: Local energy variability as a generic measure of bottom-up salience. In: Yin, P.-Y. (ed.) Pattern Recognition Techniques, Technology and Applications, In-Teh, Vienna, pp. 1–24 (2008)Google Scholar
  12. 12.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  13. 13.
    Field, D.J.: Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells. Journal of the Optical Society of America A 4(12), 2379–2394 (1987)CrossRefGoogle Scholar
  14. 14.
    Kovesi, P.: Invariant Measures of Image Features from Phase Information. Ph.D. Thesis, The University or Western Australia (1996)Google Scholar
  15. 15.
    Morrone, M.C., Burr, D.C.: Feature Detection in Human Vision: A Phase-Dependent Energy Model. Proceedings of the Royal Society of London B 235, 221–245 (1988)CrossRefGoogle Scholar
  16. 16.
    Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000)CrossRefGoogle Scholar
  17. 17.
    Zetzsche, C.: Natural Scene Statistics and Salient Visual Features. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 226–232. Elsevier, Amsterdam (2005)CrossRefGoogle Scholar
  18. 18.
    Nothdurft, H.C.: Salience of Feature Contrast. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 233–239. Elsevier, Amsterdam (2005)CrossRefGoogle Scholar
  19. 19.
    Sharma, A., Paliwal, K.K.: Fast principal component analysis using fixed-point algorithm. Pattern Recognition Letters 28, 1151–1155 (2007)CrossRefGoogle Scholar
  20. 20.
    Cortes, C., Mohri, M.: Confidence intervals for the area under the ROC curve. In: NIPS, vol. 17, p. 305 (2005)Google Scholar
  21. 21.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)zbMATHCrossRefGoogle Scholar
  22. 22.
    Ouerhani, N.: Visual Attention: Form Bio-Inspired Modelling to Real-Time Implementation, PhD thesis, University of Neuchatel (2004)Google Scholar
  23. 23.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antón Garcia-Diaz
    • 1
  • Xosé R. Fdez-Vidal
    • 1
  • Xosé M. Pardo
    • 1
  • Raquel Dosil
    • 1
  1. 1.Departamento de Electrónica e ComputaciónUniversidade de Santiago de Compostela, Grupo de Visión ArtificialSantiago de CompostelaSpain

Personalised recommendations