Model Based Selection and Classification of Local Features for Recognition Using Gabor Filters

  • Plinio Moreno
  • Alexandre Bernardino
  • José Santos-Victor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


We propose models based on Gabor functions to address two related aspects in the object recognition problem: interest point selection and classification. We formulate the interest point selection problem by a cascade of bottom-up and top-down stages. We define a novel type of top-down saliency operator to incorporate low-level object related knowledge very soon in the recognition process, thus reducing the number of canditates. For the classification process, we represent each interest point by a vector of Gabor responses whose parameters are automatically selected. Both the selection and classification procedures are designed to be invariant to rotations and scaling. We apply the approach to the problem of facial landmark classification and present experimental result illustrating the performance of the proposed techniques.


Feature Vector Saliency Function Interest Point Gabor Filter Saliency Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proc. IEEE Conf. on CVPR, pp. 1150–1157 (1999)Google Scholar
  2. 2.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE PAMI 19, 530–534 (1997)Google Scholar
  3. 3.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Weber, M., Welling, M., Perona, P.: Towards automatic discovery of object categories. In: Proc. IEEE Conf. on CVPR (2000)Google Scholar
  5. 5.
    Triggs, B.: Detecting keypoints with stable position, orientation, and scale under illumination changes. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 100–113. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE PAMI 20, 1254–1259 (1998)Google Scholar
  7. 7.
    Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Chun, M., Wolfe, J.: Visual attention. In: Goldstein, E. (ed.) Blackwell handbook of perception. Blackwell Publishers, Malden (2000)Google Scholar
  9. 9.
    Moreno, P., Bernardino, A., Santos-Victor, J.: Gabor parameter selection for local feature detection. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 11–19. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30, 79–116 (1998)CrossRefGoogle Scholar
  11. 11.
    Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30, 117–154 (1998)CrossRefGoogle Scholar
  12. 12.
    Kamarainen, J.K., Kyrki, V., Kälviäinen, H.: Fundamental frequency gabor filters for object recognition. In: Proc. of the 16th ICPR (2002)Google Scholar
  13. 13.
    Kyrki, V., Kamarainen, J.K., Kälviäinen, H.: Simple gabor feature space for invariant object recognition. Pattern Recognition Letters 25, 311–318 (2004)CrossRefGoogle Scholar
  14. 14.
    Martinez, A., Benavente, R.: The ar face database. Technical report, CVC (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Plinio Moreno
    • 1
  • Alexandre Bernardino
    • 1
  • José Santos-Victor
    • 1
  1. 1.Instituto Superior Técnico & Instituto de Sistemas e RobóticaLisboaPortugal

Personalised recommendations