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
Abstract :
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.
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.
