Clustering Approach Towards Image Segmentation: An Analytical Study

TL;DR

This paper provides an analytical review of various clustering techniques for image segmentation and demonstrates the effectiveness of K-Means clustering through an experiment on color images.

Contribution

It offers a comprehensive comparison of clustering methods for image segmentation and evaluates K-Means performance through experimental analysis.

Findings

K-Means can effectively segment color images.

Clustering methods have distinct advantages and disadvantages.

Experimental results highlight the suitability of K-Means for certain segmentation tasks.

Abstract

Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation with their pros and cons. An experiment for color image segmentation based on clustering with K-Means algorithm is performed to observe the accuracy of clustering technique for the segmentation purpose.