Advertisement

Local Color Descriptor for Object Recognition across Illumination Changes

  • Xiaohu Song
  • Damien Muselet
  • Alain Trémeau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)

Abstract

In the context of object recognition, it is useful to extract, from the images, efficient local descriptors that are insensitive to the illumination conditions, to the camera scale factor and to the position and orientation of the object. In this paper, we propose to cope with this invariance problem by applying a spatial transformation to the local regions around detected key points. The new position of each pixel after this local spatial transformation is evaluated according to both the colors and the relative positions of all the pixels in the original local region. The descriptor of the considered local region is the set of the new positions of three particular pixels in this region. The invariance and the discriminating power of our local descriptor is assessed on a public database.

Keywords

object recognition local descriptor spatial normalization discriminating power 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barnard, K., Martin, L., Coath, A., Funt, B.: A data set for colour research. Color Research and Application 27, 147–151 (2002)CrossRefGoogle Scholar
  2. 2.
    Li, J., Allinson, N.M.: A comprehensive review of current local features for computer vision. Neurocomput. 71, 1771–1787 (2008)CrossRefGoogle Scholar
  3. 3.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27, 1615–1630 (2005)CrossRefGoogle Scholar
  4. 4.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Geusebroek, J.: Compact object descriptors from local colour invariant histograms. In: British Machine Vision Conference, vol. 3, pp. 1029–1038 (2006)Google Scholar
  6. 6.
    Muselet, D., Trémeau, A.: Illumination invariant spatio-colorimetric normalization. In: Procs. of the Int. Conf. on Pattern Recognition, Tampa, Florida (2008)Google Scholar
  7. 7.
    Finlayson, G., Hordley, S., Schaefer, G., Tian, G.: Illuminant and device invariant colour using histogram equalisation. In: Procs. of the 9th IS&T/SID Color Imaging Conf., Scottsdale, USA, pp. 205–211 (2003)Google Scholar
  8. 8.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61, 103–112 (2005)CrossRefGoogle Scholar
  9. 9.
    Gershon, R., Jepson, A.D., Tsotsos, J.K.: From [r,g,b] to surface reflectance: computing color constant descriptors in images. Perception, 755–758 (1988)Google Scholar
  10. 10.
    Finlayson, G., Hordley, S., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38, 179–190 (2005)CrossRefGoogle Scholar
  11. 11.
    Finlayson, G., Schaefer, G.: Colour indexing across devices and viewing conditions. In: Procs. of the 2nd Int. Workshop on Content-Based Multimedia Indexing, Brescia, Italy, pp. 215–221 (2001)Google Scholar
  12. 12.
    Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiaohu Song
    • 1
  • Damien Muselet
    • 1
  • Alain Trémeau
    • 1
  1. 1.Lab Hubert CurienUniversité Jean MonnetSaint-EtienneFrance

Personalised recommendations