Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm

TLDR

Image segmentation classifies pixels into groups, with k‑means clustering being a widely used unsupervised method for separating foreground from background. The authors first enhance the image with partial stretching, then use subtractive clustering to generate initial centroids for k‑means, and finally apply a medial filter to clean the segmented output.

Abstract

Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is k-means clustering algorithm. K -means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. But before applying K -means algorithm, first partial stretching enhancement is applied to the image to improve the quality of the image. Subtractive clustering method is data clustering method where it generates the centroid based on the potential value of the data points. So subtractive cluster is used to generate the initial centers and these centers are used in k-means algorithm for the segmentation of image. Then finally medial filter is applied to the segmented image to remove any unwanted region from the image.