Image Disorder Characterization Based on Rate Distortion

  • Claudia Iancu
  • Inge Gavat
  • Mihai Datcu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


Rate distortion theory is one of the areas of information transmission theory with important applications in multimodal signal processing, as for example image processing, information bottleneck and steganalysis. This article present an image characterization method based on rate distortion analysis in the feature space. This space is coded using clustering as vector quantization (k-means). Since image information usually cannot be coded by single clusters, because there are image regions corresponding to groups of clusters, the rate and distortion are specifically defined. The rate distortion curve is analyzed, extracting specific features for implementing a database image classification system.


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  1. 1.
    Datcu, M., Seidel, K., D’Elia, S., Marchetti, P.G.: Knowledge - driven information mining in remote sensing image archives. ESA bulletin 110 (2002)Google Scholar
  2. 2.
    Schröder, M., Rehrauer, H., Seidel, K., Datcu, M.: Interactive learning and probabilistic retrieval in remote sensing image archives. IEEE Trans. on Geoscience and Remote Sensing 38(5), 2288–2298 (2000)CrossRefGoogle Scholar
  3. 3.
    Faur, D., Gavat, I., Datcu, M.: Mutual Information based measures for image content characterization. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 342–349. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Celik, M.U., Sharma, G., Tekalp, A.M.: Universal image steganalysis using ratedistortion curves. In: Proc. SPIE: Security, Steganography and Watermarking of Multimedia Contents VI, San Jose, USA, vol. 5306, pp. 19–22 (2004)Google Scholar
  5. 5.
    Goldberger, J., Greenspan, H., Gordon, S.: Unsupervised Image Clustering using the Information Bottleneck Method. In: The Annual Pattern Recognition Conference DAGM, Zurich (2002)Google Scholar
  6. 6.
    Tasto, M., Wintz, P.: A bound on the rate-distortion function and application to images. IEEE Transactions on Information Theory 18(1), 150–159 (1972)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Schröder, M., Walessa, M., Rehrauer, H., Seidel, K., Datcu, M.: Gibbs random field models: a toolbox for spatial information extraction. Computers and Geosciences 26, 423–432 (2000)CrossRefGoogle Scholar
  8. 8.
    Schröder, M., Rehrauer, H., Seidel, K., Datcu, M.: Spatial information retrieval from remote sensing images: Part II Gibbs Markov Random Field. IEEE Trans. On Geoscience and Remote Sensing 36, 1446–1455 (1998)CrossRefGoogle Scholar
  9. 9.
    Datcu, M., Stoichescu, D.A., Seidel, K., Iorga, C.: Model fitting and model evidence for multiscale image texture analysis. In: American Institute of Physics, AIP Conference Proceedings, vol. 735, pp. 35–42 (2004)Google Scholar
  10. 10.
    Sugar, C.A., James, G.M.: Finding the Number of Clusters in a Data Set: An Information Theoretic Approach. Journal of the American Statistical Association 98, 750–763 (2003)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Blahut, E.R.: Computation of Channel Capacity and Rate-Distortion Functions. IEEE transactions on Information Theory IT-18(4) (1972)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Claudia Iancu
    • 1
  • Inge Gavat
    • 1
  • Mihai Datcu
    • 2
  1. 1.”Politehnica” University BucharestRomania
  2. 2.German Aerospace Center DLRGermany

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