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

Author:

Nayak Nihar Ranjan1,Mishra Bikram Keshari2,Rath Amiya Kumar2,Swain Sagarika3

Affiliation:

1. Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India

2. Department of Computer Science and Engineering, VSSUT, Burla, India

3. Department of Computer Science and Engineering, Koustav Institute of Self Domain, Bhubaneswar, India

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.

Publisher

IGI Global

Subject

General Earth and Planetary Sciences,General Environmental Science

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