Image Retrieval by Colour and Texture Using Chromaticity Histograms and Wavelet Frames

  • Spyros Liapis
  • Georgios Tziritas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


In this paper the combination of texture and colour features is used for image classification. Texture features are extracted using the Discrete Wavelet Frame analysis. 2-D or 1-D histograms of the CIE Lab chromaticity coordinates are used as colour features. The 1-D histograms of the a, b coordinates were also modeled according to the generalized Gaussian distribution. The similarity measure defined on the features distribution is based on the Bhattacharya distance. Retrieval benchmarking is performed on textured colour images from natural scenes, obtained from the VisTex database of MIT Media Laboratory and from the Corel Photo Gallery.


Texture Feature Image Retrieval Colour Feature Wavelet Frame Classi Cation 
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.
    M. Flickner, H. Sawhney, W. Niblack, and J Ashley. Query by image and video content: the qbic system. IEEE Computer, 28(9):23–32, Sep 1995.Google Scholar
  2. 2.
    V. N. Gudivada and V. V. Raghavan. Content based image retrieval systems. IEEE Computer, 28, Sep 1995.Google Scholar
  3. 3.
    A. Gupta and R. Jain. Visual information retrieval. Communicastions of the A.C.M., 40(5):70–79, May 1997.Google Scholar
  4. 4.
    MIT Media Laboratory. Vistex: Texture image database.
  5. 5.
    S. Liapis, N. Alvertos, and G. Tziritas. Maximum likelihood texture classification and bayesian texture segmentation using discrete wavelet frames. Intern. Conf. in Digital Signal Processing DSP97, 2:1107–1110, July 1997.Google Scholar
  6. 6.
    F. Liu and R. W. Picard. Periodicity directionality and randomness wold features for image modeling retrieval. Pattern Recognition, 18(7):722–733, Jul 1996.Google Scholar
  7. 7.
    S. G. Malat. A theory of multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 11:674–693, January 1989.Google Scholar
  8. 8.
    V. Ogle and M. Stonebraker. Chabot: Retrieval from a relational database of images. IEEE Computer, 28(9):40–48, Sep 1995.Google Scholar
  9. 9.
    A. Pentland, R. W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. M.I.T. Media Laboratory Perceptual Computing Technical Report No. 255, November 1993.Google Scholar
  10. 10.
    J. Puzicha, J. Buhmann, Y. Rubner, and C. Tomasi. Empirical evaluation of dissimilarity measures for color and texture. Intern. Conf. on Computer Vision, Sep 1999.Google Scholar
  11. 11.
    M. Unser. Texture classification and segmentation using wavelet frames. IEEE Trans. on Image Processing, 4:1549–1560, November 1995.Google Scholar
  12. 12.
    T. Young and K.S. Fu. Handbook of pattern recognition and image processing. Academic Press, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Spyros Liapis
    • 1
  • Georgios Tziritas
    • 1
  1. 1.Computer Science DepartmentUniversity of CreteHeraklionGreece

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