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Fast Color Quantization via Fuzzy Clustering

  • László SzilágyiEmail author
  • Gellért Dénesi
  • Călin Enăchescu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

This comparative study employs several modified versions of the fuzzy c-means algorithm in image color reduction, with the aim of assessing their accuracy and efficiency. To assure equal chances for all algorithms, a common framework was established that preprocesses input images in terms of a preliminary color quantization, extraction of histogram and selection of frequently occurring colors of the image. Selected colors were fed to clustering by studied c-means algorithm variants. Besides the conventional fuzzy c-means (FCM) algorithm, the so-called generalized improved partition FCM algorithm, and several versions of the generalized suppressed FCM were considered. Accuracy was assessed by the average color difference between input and output images, while efficiency tests monitored the total runtime. All modified algorithms were found more accurate, and some suppressed models also faster than FCM.

Keywords

Color quantization Fuzzy clustering Improved partition 

References

  1. 1.
    Rasti, J., Monadjemi, A., Vafaei, A.: Color reduction using a multi-stage Kohonen self-organizing map with redundant features. Exp. Syst. Appl. 38, 13188–13197 (2011)CrossRefGoogle Scholar
  2. 2.
    Celebi, M.E., Wen, Q., Schaefer, G., Zhou, H.: Batch neural gas with deterministic initialization for color quantization. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 48–54. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    El-Said, S.A.: Image quantization using improved artificial fish swarm algorithm. Soft Comput. 19, 2667–2679 (2015)CrossRefGoogle Scholar
  4. 4.
    Yue, X.D., Miao, D.Q., Cao, L.B., Wu, Q., Chen, Y.F.: An efficient color quantization based on generic roughness measure. Patt. Recogn. 47, 1777–1789 (2014)CrossRefzbMATHGoogle Scholar
  5. 5.
    Celebi, M.E.: Improving the performance of \(k\)-means in color quantization. Image Vis. Comput. 29, 260–271 (2011)CrossRefGoogle Scholar
  6. 6.
    Szilágyi, L., Dénesi, G., Szilágyi, S.M.: Fast color reduction using approximative \(c\)-means clustering models. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 194–201 (2014)Google Scholar
  7. 7.
    Celebi, M.E., Wen, Q., Hwang, S.: An effective real-time color quantization method based on divisive hierarchical clustering. J. Real-Time Imag. Process. 10, 329–344 (2015)CrossRefGoogle Scholar
  8. 8.
    Zeng, S., Huang, R., Kang, Z.: Image retrieval using spatiograms of colors quantized by Gaussian Mixture models. Neurocomputing 171, 673–684 (2016)CrossRefGoogle Scholar
  9. 9.
    Schaefer, G.: Soft computing-based colour quantisation. EURASIP J. Imag. Video Process. 2014(8), 1–9 (2014)Google Scholar
  10. 10.
    Höppner, F., Klawonn, F.: Improved fuzzy partition for fuzzy regression models. Int. J. Approx. Reason. 5, 599–613 (2003)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Zhu, L., Chung, F.L., Wang, S.: Generalized fuzzy \(c\)-means clustering algorithm with improved fuzzy partition. IEEE Trans. Syst. Man Cybern. B. 39, 578–591 (2009)CrossRefGoogle Scholar
  12. 12.
    Fan, J.L., Zhen, W.Z., Xie, W.X.: Suppressed fuzzy \(c\)-means clustering algorithm. Patt. Recogn. Lett. 24, 1607–1612 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Szilágyi, L., Szilágyi, S.M.: Generalization rules for the suppressed fuzzy \(c\)-means clustering algorithm. Neurocomputing 139, 298–309 (2014)CrossRefGoogle Scholar
  14. 14.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • László Szilágyi
    • 1
    • 2
    Email author
  • Gellért Dénesi
    • 1
  • Călin Enăchescu
    • 3
  1. 1.Faculty of Technical and Human Science of Tîrgu MureşSapientia - Hungarian Science University of TransylvaniaTîrgu MureşRomania
  2. 2.Department of Control Engineering and Information TechnologyBudapest University of Technology and EconomicsBudapestHungary
  3. 3.Department of InformaticsPetru Maior University of Tîrgu MureşTîrgu MureşRomania

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