A novel approach for color image segmentation using iterative partitioning mean shift clustering algorithm

Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.

I. Introduction

Segmenting an image is an effort to group similar colors or elements of an image into a cluster or group. This can be achieved by clustering, which clusters the number of colors or elements into several clusters based on the similarity of color intensities and gray intensities of an image. Main objective of clustering an image is dominant colors extraction from the images. By extracting the information from images such as texture, color, shape and structure, the image segmentation can be very important to simplify. Because of the information extraction in any images, the segmentation has been used in many fields such as Enhancing the image, compression, retrieval systems i.e., search engines, object detection, and medical image processing.