Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals

This research proposes the method to extract the characteristics of images to become the
intervals. These intervals are used to build the automatic fuzzy genetic algorithm for images
(AFGI). In the proposed model, the overlap measure is the criterion to evaluate the closeness
of intervals, and the new Davies and Bouldin index is the objective function. The AFGI
can determine the proper number of clusters, the images in each cluster, and the probability
to belong to clusters of images at the same time. The experiments with different types of
images illustrate the steps of AFGI, and show its significant benefit in comparing to other
algorithms.