Automatic Determination Number of Cluster for NMKFC-Means Algorithms on Image Segmentation

Abstract

Image segmentation plays an important role in image analysis. Image segmentation is useful in many applications like medical, face recognition, crop disease detection, and geographical object detection in map. Image segmentation is performed by clustering method. Clustering method is divided into Crisp and Fuzzy clustering methods. FCM is famous method used in fuzzy clustering to improve result of image segmentation. FCM does not work properly in noisy and nonlinear separable image, to overcome this drawback, KFCM method for image segmentation can be used. In KFCM method, Gaussian kernel function is used to convert nonlinear separable data into linear separable data and high dimensional data and then apply FCM on this data. KFCM is improving result of noisy image segmentation. KFCM improves accuracy rate but does not focus on neighbor pixel. NMKFCM method incorporates neighborhood pixel information into objective function and improves result of image segmentation. New proposed algorithm is effective and efficient than other fuzzy clustering algorithms and it has better performance in noisy and noiseless images. In noisy image, find automatically required number of cluster with the help of Hill-climbing algorithm. Keyword: Component: Clustering, Fuzzy clustering, FCM, Hill-climbing algorithm, KFCM, NMKFCM