Dinh PhamtoanTai Vovan2022-11-022022-11-022020https://repository.vlu.edu.vn/handle/123456789/60110.1007/s11042-020-09975-3This 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.en-US"Cluster analysisFuzzy genetic algorithmImage processingInterval dataPattern recognitionUnsupervised learning"journal-article