Effective Color Image Retrieval Based on the Gaussian Mixture Model

  • Maria Luszczkiewicz-Piatek
  • Bogdan Smolka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)

Abstract

The main problem addressed in this paper is as follows: a system applying the proposed framework should retrieve all images whose color structure is similar to that of the given query image, independently on the applied lossy coding. We propose an approach based on the color histogram approximation using the Gaussian Mixture Model. The proposed method incorporates the information on the spatial distribution of the color image pixels utilizing the bilateral filtering scheme. The retrieval results were evaluated on large databases of natural color images and the usefulness of the proposed technique was compared with some commonly known retrieval methods operating on color histograms.

Keywords

color image retrieval lossy compression Gaussian mixture 

References

  1. 1.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
  2. 2.
    Zhou, X.S., Rui, Y., Huang, T.S.: Exploration of Visual Data. Kluwer, Dordrecht (2003)CrossRefMATHGoogle Scholar
  3. 3.
    Pass, G., Zabih, R.: Comparing images using joint histograms. Journal of Multimedia Systems 7(3), 234–240 (1999)CrossRefGoogle Scholar
  4. 4.
    Ciocca, G., Schettini, L., Cinque, L.: Image Indexing and Retrieval Using Spatial Chromatic Histograms and Signatures. In: Proc. of CGIV, pp. 255–258 (2002)Google Scholar
  5. 5.
    Lambert, P., Harvey, N., Grecu, H.: Image Retrieval Using Spatial Chromatic Histograms. In: Proc. of CGIV, pp. 343–347 (2004)Google Scholar
  6. 6.
    Hartut, T., Gousseau, Y., Schmitt, F.: Adaptive Image Retrieval Based on the Spatial Organization of Colors. Computer Vision and Image Understanding 112, 101–113 (2008)CrossRefGoogle Scholar
  7. 7.
    Heidemann, G.: Combining Spatial and Colour Information For Content Based Image Retrieval. Computer Vision and Image Understanding 94, 234–270 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Trans. Patt. Anal. Mach. Intel. 9, 947–963 (2001)CrossRefGoogle Scholar
  9. 9.
    Rugna, J.D., Konik, H.: Color Coarse Segmentation and Regions Selection for Similar Images Retrieval. In: Proc. of CGIV, pp. 241–244 (2002)Google Scholar
  10. 10.
    Dvir, G., Greenspan, H., Rubner, Y.: Context-Based Image Modelling. In: Proc. of ICPR, pp. 162–165 (2002)Google Scholar
  11. 11.
    Jing, F., Li, M., Zhang, H.J.: An Effective Region-Based Image Retrieval Framework. IEEE Trans. on Image Processing 13(5), 699–709 (2004)CrossRefGoogle Scholar
  12. 12.
    Berretti, A., Del Bimbo, E.: Weighted Walktroughs Between Extended Entities for Retrieval by Spatial Arrangement. IEEE Trans. on Multimedia 3(1), 52–70 (2002)Google Scholar
  13. 13.
    Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from incomplete data. J. Royal Stat. Soc. 39B, 1–38 (1977)MATHGoogle Scholar
  14. 14.
    McLachlan, G., Peel, D.: Finite Mixtures Models. John Wiley & Sons, Chichester (2000)CrossRefMATHGoogle Scholar
  15. 15.
    Kuo, W.-J., Chang, R.-F.: Approximating the Statistical Distribution of Color Histogram for Content-based Image Retrieval. In: Proc. of ICASP, vol. 4, pp. 2007–2010 (2000)Google Scholar
  16. 16.
    Jeong, S., Won, C.-S., Gray, R.M.: Image Retrieval Using Color Histograms Generated by Gauss Mixture Vector Quantization. Comp. Vis. Image Understanding 94(1-3), 44–66 (2004)CrossRefGoogle Scholar
  17. 17.
    Luszczkiewicz, M., Smolka, B.: Gaussian Mixture Model Based Retrieval Technique for Lossy Compressed Color Images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 662–673. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Luszczkiewicz, M., Smolka, B.: A Robust Indexing and Retrieval Method for Lossy Compressed Color Images. In: Proc. of IEEE International Symposium on Image and Signal, Processing and Analysis, pp. 304–309 (2007)Google Scholar
  19. 19.
    Luszczkiewicz, M., Smolka, B.: Spatial Color Distribution Based Indexing and Retrieval Scheme. Advances in Soft Computing 59, 419–427 (2009)CrossRefMATHGoogle Scholar
  20. 20.
    Luszczkiewicz, M., Smolka, B.: Application of Bilateral Filtering and Gaussian Mixture Modeling for the Retrieval of Paintings. In: Proc. of ICIP, pp. 77–80 (2009)Google Scholar
  21. 21.
    Smolka, B., Szczepanski, M., Lukac, R., Venetsanoloulos, A.N.: Robust Color Image Retrieval for the World Wide Web. In: Proc. of ICASSP, pp. 461–464 (2004)Google Scholar
  22. 22.
    Elad, M.: On the Origin of the Bilateral Filter and Ways to Improve It. IEEE Trans. on Image Processing 11(10), 1141–1151 (2002)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Paris, S., Durand, F.: A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach. Int. J. Comput. Vision 39B, 1–38 (2007)Google Scholar
  24. 24.
    Alata, O., Quintard, L.: Is There a Best Color Space for Color Image Characteriation or Representation Based on Multivariate Gaussian Mixture Model? Comp. Vis. Image Understanding 113, 867–877 (2009)CrossRefGoogle Scholar
  25. 25.
    Kuehni, R.G.: Color Space and Its Divisions. John Wiley & Sons, Chichester (2003)CrossRefGoogle Scholar
  26. 26.
    Bilmes, J.: A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. University of Berkeley, ICSI-TR-97-021 (1997)Google Scholar
  27. 27.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A Metric for Distributions with Applications to Image Databases. In: Proc. of ICCV, pp. 59–66 (1998)Google Scholar
  28. 28.
    Chatzichristofis, S.A., Boutalis, Y.S., Lux M.: IMG(RUMMAGER): An Interactive Content Based Image Retrieval System. In: Proc. of the 2nd International Workshop on Similarity Search and Applications (SISAP), pp. 151–153 (2009)Google Scholar
  29. 29.
    Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: EDD: Color and Edge Directivity Descriptor, a Compact Descriptor for Image Indexing and Retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: FCTH: Fuzzy Color and Texture Histogram - a Low Level Feature for Accurate Image Retrieval. In: Proc. of the 9th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), pp. 191–196 (2008)Google Scholar
  31. 31.
    Manjunath, B.S., Ohm, J.R., Vasudevan, V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. Cir. Sys. Video Technology 11, 703–715 (1998)CrossRefGoogle Scholar
  32. 32.
    Tamura, S.M.H., Yamawaki, T.: Textural Features Corresponding to Visual Perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–472 (1978)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maria Luszczkiewicz-Piatek
    • 1
  • Bogdan Smolka
    • 2
  1. 1.Faculty of Mathematics and Computer Science, Department of Applied Computer ScienceUniversity of LodzLodzPoland
  2. 2.Department of Automatic ControlSilesian University of TechnologyGliwicePoland

Personalised recommendations