Image Retrieval Based on Gaussian Mixture Approach to Color Localization

  • Maria Luszczkiewicz-Piatek
  • Bogdan Smolka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

The paper focuses on the possibilities of color image retrieval of the images sharing the similar location of particular color or set of colors present in the depicted scene. The main idea of the proposed solution is based on treating image as a multispectral object, where each of its spectral channels shows locations of pixels of 11 basis colors within the image. Thus, each of the analyzed images has associated signature, which is constructed on the basis of the mixture approximation of its spectral components. The ability of determining of highly similar images, in terms of one or more basic colors, reveals that the proposed method provides useful and efficient tool for robust to impulse distortions image retrieval.

Keywords

color image retrieval color composition 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.
    Huang, J., et al.: Spatial Color Indexing and Applications. International Journal of Computer Vision 35(3), 245–268 (1999)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Pass, G., Zabih, R.: Comparing images using joint histograms. Journal of Multimedia Systems 7(3), 234–240 (1999)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Lambert, P., Harvey, N., Grecu, H.: Image Retrieval Using Spatial Chromatic Histograms. In: Proc. of CGIV, pp. 343–347 (2004)Google Scholar
  7. 7.
    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
  8. 8.
    Heidemann, G.: Combining Spatial and Colour Information For Content Based Image Retrieval. Computer Vision and Image Understanding 94, 234–270 (2004)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Dvir, G., Greenspan, H., Rubner, Y.: Context-Based Image Modelling. In: Proc. of ICPR, pp. 162–165 (2002)Google Scholar
  12. 12.
    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
  13. 13.
    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
  14. 14.
    McLachlan, G., Peel, D.: Finite Mixtures Models. John Wiley & Sons, Chichester (2000)CrossRefMATHGoogle Scholar
  15. 15.
    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
  16. 16.
    Van den Broek, E.L., Schouten, T.E., Kisters, P.M.F.: Modeling human color categorization. Pattern Recogn. Lett. 29(8), 1136–1144 (2008)CrossRefGoogle Scholar
  17. 17.
    Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from incomplete data. J. Royal Stat. Soc. 39B, 1–38 (1977)MATHGoogle Scholar
  18. 18.
    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
  19. 19.
    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
  20. 20.
    Luszczkiewicz, M., Smolka, B.: Spatial Color Distribution Based Indexing and Retrieval Scheme. In: Advances in Soft Computing, vol. 59, pp. 419–427 (2009)Google Scholar
  21. 21.
    Luszczkiewicz, M., Smolka, B.: Application of Bilateral Filtering and Gaussian Mixture Modeling for the Retrieval of Paintings. In: Proc. of PODKA, vol. 3, pp. 77–80 (2009)Google Scholar
  22. 22.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)CrossRefMATHGoogle 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