Silhouette index for determining optimal k-means clustering on images in different color models

  • Authors

    • Abd Rasid Mamat
    • Fatma Susilawati Mohamed
    • Mohamad Afendee Mohamed
    • Norkhairani Mohd Rawi
    • Mohd Isa Awang
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.14.11464
  • Cluster validation, Color model, Image filtering, K-means algorithm, Silhouette index.

  • Abstract

    Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

     

     

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  • How to Cite

    Rasid Mamat, A., Susilawati Mohamed, F., Afendee Mohamed, M., Mohd Rawi, N., & Isa Awang, M. (2018). Silhouette index for determining optimal k-means clustering on images in different color models. International Journal of Engineering & Technology, 7(2.14), 105-109. https://doi.org/10.14419/ijet.v7i2.14.11464

    Received date: 2018-04-12

    Accepted date: 2018-04-12

    Published date: 2018-04-06