Color Image Segmentation Using a Weighted kernel-based Fuzzy C- Means Algorithm

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.