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Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram

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  • Lamine Benrais

    (LRIA Laboratory, Computer Science Department, University of Science and Technology Houari Boumediene, Algiers, Algeria)

  • Nadia Baha

    (LRIA Laboratory, Computer Science Department, University of Science and Technology Houari Boumediene, Algiers, Algeria)

Abstract

The K-means is a popular clustering algorithm known for its simplicity and efficiency. However the elapsed computation time is one of its main weaknesses. In this paper, the authors use the K-means algorithm to segment grayscale images. Their aim is to reduce the computation time elapsed in the K-means algorithm by using a grayscale histogram without loss of accuracy in calculating the clusters centers. The main idea consists of calculating the histogram of the original image, applying the K-means on the histogram until the equilibrium state is reached, and computing the clusters centers then the authors use the clusters centers to run the K-means for a single iteration. Tests of accuracy and computational time are presented to show the advantages and inconveniences of the proposed method.

Suggested Citation

  • Lamine Benrais & Nadia Baha, 2016. "Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 8(2), pages 57-69, April.
    • Handle: RePEc:igg:jisss0:v:8:y:2016:i:2:p:57-69
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