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An Enhanced Clustering Method for Image Segmentation

An Enhanced Clustering Method for Image Segmentation

Bikram Keshari Mishra, Amiya Kumar Rath
Copyright: © 2019 |Pages: 19
ISBN13: 9781522558323|ISBN10: 1522558322|ISBN13 Softcover: 9781522588474|EISBN13: 9781522558330
DOI: 10.4018/978-1-5225-5832-3.ch016
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MLA

Mishra, Bikram Keshari, and Amiya Kumar Rath. "An Enhanced Clustering Method for Image Segmentation." Exploring Critical Approaches of Evolutionary Computation, edited by Muhammad Sarfraz, IGI Global, 2019, pp. 325-343. https://doi.org/10.4018/978-1-5225-5832-3.ch016

APA

Mishra, B. K. & Rath, A. K. (2019). An Enhanced Clustering Method for Image Segmentation. In M. Sarfraz (Ed.), Exploring Critical Approaches of Evolutionary Computation (pp. 325-343). IGI Global. https://doi.org/10.4018/978-1-5225-5832-3.ch016

Chicago

Mishra, Bikram Keshari, and Amiya Kumar Rath. "An Enhanced Clustering Method for Image Segmentation." In Exploring Critical Approaches of Evolutionary Computation, edited by Muhammad Sarfraz, 325-343. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5832-3.ch016

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Abstract

The findings of image segmentation reflect 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 chapter, the authors have evaluated the performances of three different clustering algorithms used in image segmentation: the classical k-means, its modified k-means++, and proposed enhanced clustering method. Brief explanations of the fundamental working principles implicated in these methods are presented. Thereafter, the performance which affects the outcome of segmentation are evaluated considering two vital quality measures, namely structural content (SC) and root mean square error (RMSE). 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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