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 sum 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 segmentation of image 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 decrements the similarity matrix size and there by enhances DP algorithm. Secondly, the Density Balance technique generates a stable decision graph, which enables the DP algorithm for a fully autonomous clustering. Lastly, to improve image segmentation outcomes, the system which works on prior entropy employs a Fuzzy c-means clustering. Through this, information of pixels in spatial neighbors are considered and can see improved segmentation results. In the present work, an attempt is made to develop and explain the segmentation of images using Automatic Fuzzy Clustering Framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Segmentation of Images using Automatic Fuzzy Clustering Framework




    Publication date :

    2021-12-02


    Size :

    430811 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SEGMENTATION OF EIT IMAGES USING FUZZY CLUSTERING: A PRELIMINARY STUDY

    Barra, V. / Delouille, V. / Hochedez, J.-F. et al. | British Library Conference Proceedings | 2005


    Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering

    Barra, V. / Delouille, V. / Hochedez, J. F. et al. | British Library Conference Proceedings | 2008



    Automatic Segmentation of Liver Tumour using a Possibilistic Alternative Fuzzy C-Means Clustering

    Kumar, Sikamony S. / Moni, Rama S. / Rajeesh, Jayapathy | British Library Online Contents | 2013


    A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination

    Colantonio, S. / Salvetti, O. / Gurevich, I. B. | British Library Online Contents | 2007