Author Description

Clustering is grouping up of data points. Using clustering algorithms, the data points can be grouped with similar properties. Fuzzy clustering is grouping of data points of clusters of one or more clusters. Density Peak (DP) clustering can find the clusters but when the number of clusters is increased, it suffers memory overflow, because when a normal size image is used for image segmentation which contains a greater number of pixels, it results in a high degree of similarity matrix. To avoid this, Automatic Fuzzy Clustering Framework (AFCF) for image segmentation could be introduced. This framework contributes in three ways. To begin with, the density peak method is used for the concept of super pixel, which reduces the size of the similarity matrix and so enhances the DP algorithm computational efficiency. Secondly, the usage of density balance technique to generate a stable decision graph, which enables the DP algorithm in fully autonomous clustering. Lastly, to improve image segmentation outcomes, the system employs a fuzzy c-means clustering based on prior entropy. By this, the information of pixels of spatial neighbors are considered and can see improved segmentation results. In the present book, an attempt is made to develop and explain the segmentation of image using Automatic Fuzzy Clustering Framework.