Global Journal of Science, Engineering and Technology (ISSN: 2322-2441) Issue 14, 2013, pp. 132-141 © GJSET Publishing, 2013. http://www.gjset.org 132 Color Image Segmentation Using a Weighted kernel-based Fuzzy C- Means Algorithm Siavash Alipour 1 , Mousa Nazari 2 , Jamshid Shanbehzadeh 3 1,2 student with Department of Computer Engineering Kharazmi University 1 Siavash.alipour@tmu.ac.ir , 2 Nazari.mousa@gmail.com 3 Associate Professor with Department of Computer Engineering Kharazmi University, jamside@tmu.ac.ir Abstract: Color image segmentation plays an important role in computer vision and image processing applications. Kernel-based fuzzy C-means (KFCM) is well known and powerful methods used in image segmentation. Moreover, an appropriate assigning weight to features can improve its performance. This paper focuses on improving the image segmentation capabilities of KFCM based on feature weighting. It employs Entropy concept to measure the weight of features based on statistical variations viewpoint in KFCM. We compare the segmentation results of the proposed method with the well know algorithms along the same line that used weight selection procedure in FCM algorithm. Our simulation results reveal that the proposed algorithm provides greater segmentation performance for color image segmentation according to cluster validity function. Keywords: kernel-based fuzzy C-means; Entropy; FCM; weight selection; Variation; Color image segmentation 1. Introduction Color image segmentation plays an important role in computer vision and image processing applications. Segmentation is to partition an image into regions that are homogeneous according to a criterion, such as intensity, color, tone, texture and, etc [1]and, it is considered as one of the most difficult tasks in image processing and affects the quality of the final results of an image processing or machine vision system. Generally, the gray level image segmentation techniques can be divided into four categories, thresholding, clustering, edge detection, and region extraction [2]. At present, these techniques can be extended to color images expressed in different color spaces. These spaces are obtained by using the linear and non-linear transformations of the RGB color space. Many different techniques of color image segmentation have been developed and detailed in the literature [3]. Nowadays, the clustering methods [4, 5] are considered as one type of fundamental tool for image segmentation. In clustering methods where clustering numbers are known in prior, fuzzy C-Means (FCM) algorithm is one of the most widely used methods due to its flexibility in clustering pixels of segmented