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An Enhanced Clustering Method for Image Segmentation
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Author(s): Bikram Keshari Mishra (Veer Surendra Sai University of Technology, India)and Amiya Kumar Rath (Veer Surendra Sai University of Technology, India)
Copyright: 2019
Pages: 19
Source title:
Exploring Critical Approaches of Evolutionary Computation
Source Author(s)/Editor(s): Muhammad Sarfraz (Kuwait University, Kuwait)
DOI: 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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