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)


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.


Color quantization Fuzzy clustering Improved partition 


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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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