Journey on image clustering based on color composition

Achmad Nizar Hidayanto, Elisabeth Martha Koeanan

Research output: Contribution to journalArticlepeer-review


Image clustering is a process of grouping images based on their similarity. The image clustering usually uses the color component, texture, edge, shape, or mixture of two components, etc. This research aims to explore image clustering using color composition. In order to complete this image clustering, three main components should be considered, which are color space, image representation (feature extraction), and clustering method itself. We aim to explore which composition of these factors will produce the best clustering results by combining various techniques from the three components. The color spaces use RGB, HSV, and L*a*b* method. The image representations use Histogram and Gaussian Mixture Model (GMM), whereas the clustering methods use KMeans and Agglomerative Hierarchical Clustering algorithm. The results of the experiment show that GMM representation is better combined with RGB and L*a*b* color space, whereas Histogram is better combined with HSV. The experiments also show that K-Means is better than Agglomerative Hierarchical for images clustering.

Original languageEnglish
Pages (from-to)954-959
Number of pages6
JournalWorld Academy of Science, Engineering and Technology
Publication statusPublished - 2010


  • Agglomerative Hierarchical Clustering (AHC)
  • Expectation-Maximization (EM)
  • Feature extraction
  • Gaussian Mixture Model (GMM)
  • HSV
  • Histogram
  • Image clustering
  • K-Means
  • L*a*b*
  • RGB


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