A Generalisation Of The Fuzzy C-means Clustering Algorithm

Abstract

Several algorithms have been defined which can segment images, each algorithm having its own merits. The Maximum Likelihood (ML) algorithm is considered the most accurate, while the Fuzzy c-Means (FCM) algorithm converges more quickly. This paper describes a generalisation of the FCM algorithm (GFCM) which is more versatile than the standard FCM, having discriminant functions which may, by changing parameters, be varied in order to suit particular applications. The discriminant functions can thus be more realistic than those used in the standard FCM and in the limiting case they approach gaussians, where the algorithm produces results identical to some implementations of ML.