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Madhusmita Sahu
Centurion University of Technology and Management, Odisha, India
Computer Science & IT Electrical & Electronics Engineering

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Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images Madhusmita Sahu; K. Parvathi; M. Vamsi Krishna
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (899.789 KB) | DOI: 10.11591/ijece.v7i2.pp810-817

Abstract

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.    
Co-Authors K. Parvathi M. Vamsi Krishna