A Survey on Fuzzy C-Means Clustering Algorithm for Image Segmentation

Image segmentation is one of the key techniques in image understanding and computer vision. The work of image segmentation is to divide an image into a number of non-overlapping regions, which have same characteristics such as gray level, color, tone, texture, etc. A various clustering based methods have been proposed for image segmentation. This paper discusses about categories of Fuzzy clustering algorithm and its problem in each algorithm.

The proposed system present an improved Fuzzy C-means (FCM) algorithm for image segmentation by introducing a trade-off weighted fuzzy factor and a kernel metric. The trade-off weighted fuzzy factor depends on the both space distance of all neighboring pixels and their gray-level difference. This new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhancement its robustness to noise and outliers, this new system introduce a kernel distance measure to its objective function. The new algorithm determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Further the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free.