Work

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

Year: 2017

Type: article

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.

Source: International Journal of Electrical and Computer Engineering (IJECE)

DOI: 10.11591/ijece.v7i2.pp810-817

Authors: Madhusmita Sahu, K. Parvathi, M. Vamsi Krishna

Institutions: Centurion University of Technology and Management, KIIT University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2592507341

FWCI: 0.326

Cites: 8

Cited by: 5

Related to: 10