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

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

Lamine Benrais, Nadia Baha
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 13
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781466689794|DOI: 10.4018/IJISSS.2016040105
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MLA

Benrais, Lamine, and Nadia Baha. "Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram." IJISSS vol.8, no.2 2016: pp.57-69. http://doi.org/10.4018/IJISSS.2016040105

APA

Benrais, L. & Baha, N. (2016). Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram. International Journal of Information Systems in the Service Sector (IJISSS), 8(2), 57-69. http://doi.org/10.4018/IJISSS.2016040105

Chicago

Benrais, Lamine, and Nadia Baha. "Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram," International Journal of Information Systems in the Service Sector (IJISSS) 8, no.2: 57-69. http://doi.org/10.4018/IJISSS.2016040105

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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.

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