Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means

  • Perani Rosyani * Mail Universitas Pamulang, Tangerang Selatan, Indonesia
  • A Suhendi Universitas Pamulang, Tangerang Selatan, Indonesia
  • D H Apriyanti LIPI, Jawa Timur, Indonesia
  • A A Waskita PPIKSN-BATAN, Tangerang Selatan, Indonesia
  • (*) Corresponding Author
Keywords: Flower Image Segmentation; K-Means; Fuzzy C-Means; Color Features; Blob Analysis; Hausdorff Distance


A more detail investigation of color feature for flower segmentation using K-means and fuzzy C-means was conducted in this paper. The sample images containing 1, 2, 3, 4 dianthus del- toides L flowers, obtained from ImageCLEF 2017 will be used. K-means and fuzzy C-means will use different color model components as the feature for segmenting the flower objects from their background while keeping the value of k for K-means and fuzzy C-means constant. Then the performance of the segmentation approaches will be evaluated by using the ground truth infor- mation. The evaluation parameters involved are Hausdorff distance and a number of classifier performance metrics such as accuracy, error rate, sensitivity and specivicity. It is shown that the segmentation process will greatly influenced by the use of LAB color model components


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Article History
Submitted: 2021-12-19
Published: 2021-12-31
Abstract View: 26 times
PDF Download: 5 times
How to Cite
Rosyani, P., Suhendi, A., Apriyanti, D. H., & Waskita, A. A. (2021). Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means. Building of Informatics, Technology and Science (BITS), 3(3), 253-259.

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