Color Image Retrieval Based on Mixture Approximation and Color Region Matching

  • Maria Luszczkiewicz-Piatek
  • Bogdan Smolka
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


The paper introduces the extension of the color image retrieval method based on the approximation of the perceptual parameters. The proposed solution enables effective search for similar images regardlessly of the applied compression scheme not only taking into account the color palette and the presence of regions of the homogenous color within the image, but also their spatial arrangement. The proposed method utilizes the Gaussian Mixture Modeling combined with the Bilateral Filtering approach along with color matching method based on dominant region color. The evaluated results show that satisfactory retrieval results can be obtained regardlessly to applied compression schemes, preserving the spatial arrangement of the color regions in evaluated results.


Gaussian Mixture Model Image Retrieval Query Image Color Histogram Retrieval Result 
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.
    Achanta, R., Estrada, F., Wils, P., Sausstrun, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Antani, S., Kasturi, R., Jain, R.: A survey of the use of pattern recognition method for abstraction, indexing and retrieval. Patern Recognition 1, 945–965 (2002)CrossRefGoogle Scholar
  3. 3.
    Bilmes, J.: A Gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian Mixture and Hidden Markov Models. Technical Report, University of Berkeley, ICSI-TR-97-021 (1997)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistics Society 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing 11(10), 1141–1151 (2002)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Gray, R.M.: Gauss mixture vector quantization. In: Proceedings of IEEE ICASSP, vol. 3, pp. 1769–1772 (2005)Google Scholar
  7. 7.
    Jeong, S., Won, C. -S., Gray, R.M.: Image retrieval using color histograms generated by Gauss mixture vector quantization. Computer Vision and Image Understanding 94(1-3), 44–66 (2004)CrossRefGoogle Scholar
  8. 8.
    Jeong, S.: Distributional distances in color image retrieval with GMVQ-generated histograms. LNCS, vol. 3569, pp. 465–475. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Liu, Y., Zhang, D., Lu, D., Ma, W.Y.: A survey of content-based image retrieval with high level sementics. Patern Recognition 40, 262–282 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    Luszczkiewicz, M., Smolka, B.: Application of bilateral filtering and Gaussian Mixture modeling for the retrieval of paintings. In: Proceedings of the International Conference on image Processing (ICIP 2009), pp. 77–80 (2009)Google Scholar
  12. 12.
    McLachlan, G., Peel, D.: Finite Mixtures Models. John Wiley & Sons, Chichester (2000)CrossRefGoogle Scholar
  13. 13.
    Ohashi, T., Aghbari, Z., Makinouchi, A.: Hill-climbing algorithm for efficient color-based image segmentation. In: Proc. of IAESTED International Conference on Signal Processing, Pattern Recognition, and Applications (2003)Google Scholar
  14. 14.
    Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Application. Springer, Berlin (2000)Google Scholar
  15. 15.
    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)CrossRefzbMATHGoogle Scholar
  16. 16.
    Vasconcelos, N.: Minimum probability of error image retrieval. IEEE Transactions on Signal Processing 52(8), 2322–2336 (2004)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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