Fuzzy C-Means Clustering Using Principal Component Analysis for Image Segmentation

Authors : Anju Bala; Aman Kumar Sharma

Volume/Issue : Volume 8 - 2023, Issue 7 - July

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/yth2bdj8

DOI : https://doi.org/10.5281/zenodo.8195303

Nowadays, Image segmentation is the area in which most of the research is carried out. It is considered as one of the most crucial fields in image analysis. It is used to divide an image into meaningful regions and thus extract the region of interests. These regions are considered as objects. Fuzzy c-means (FCM) clustering is one of the best clustering method used for image segmentation, but have a drawback of unknown cluster number. This paper focuses on this drawback of FCM and to overcome it, the Principal component analysis (PCA) is used. PCA is used for detection of cluster numbers for FCM because of its dimension reduction capability. The cluster number is the important factor on which the clustering result depends. Experimental results show that the proposed method efficiently calculate the cluster number for different test images and gives effective results.

Keywords : Clustering, Cluster Number, Fuzzy C-Means, Image Compression, Image Segmentation, Principal Component Analysis.


Paper Submission Last Date
30 - November - 2023

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