Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Madhusmita Sahu (Centurion University of Technology and Management, Odisha, India)
K. Parvathi (KIIT University, Bhubaneswar, India)
M. Vamsi Krishna (Centurion University of Technology and Management, Odisha, India)

International Journal of Electrical and Computer Engineering
Vol 7, No 2: April 2017

Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation.