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Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology

Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology

Nihar Ranjan Nayak, Bikram Keshari Mishra, Amiya Kumar Rath, Sagarika Swain
Copyright: © 2017 |Pages: 15
ISBN13: 9781522509837|ISBN10: 1522509836|EISBN13: 9781522509844
DOI: 10.4018/978-1-5225-0983-7.ch075
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MLA

Nayak, Nihar Ranjan, et al. "Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology." Biometrics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1788-1802. https://doi.org/10.4018/978-1-5225-0983-7.ch075

APA

Nayak, N. R., Mishra, B. K., Rath, A. K., & Swain, S. (2017). Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology. In I. Management Association (Ed.), Biometrics: Concepts, Methodologies, Tools, and Applications (pp. 1788-1802). IGI Global. https://doi.org/10.4018/978-1-5225-0983-7.ch075

Chicago

Nayak, Nihar Ranjan, et al. "Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology." In Biometrics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1788-1802. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0983-7.ch075

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Abstract

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

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