Implementation of Image Segmentation for Natural Images using Clustering Methods

Natural image is one of the fundamental problems in image processing and Computer Vision. Image segmentation is the process of partitioning an image into multiple meaningful regions or sets of pixels with respect to a particular application. Image segmentation is a critical and essential component of image analysis system. In literature, there are many image segmentation techniques. One of the most important techniques is Clustering methods for natural image segmentation. Clustering methods were one of the first techniques used for the segmentation of natural images. Clustering in image segmentation is defined as the process of identifying groups of similar image primitive. The purpose of clustering is to get meaningful result, effective storage and fast retrieval in various fields. In literature, there are many Clustering methods for natural image segmentation. In this paper, we used three Clustering methods to implement and comparisons between them for segmentation of Natural images and they are K-Means clustering, K-Medoids clustering and Hierarchical clustering. Keywords— Clustering Methods, Hierarchical Clustering, K-Means Clustering, K-Medoids Clustering, MATLAB, Natural Image Segmentation.