Comparison and Analysis of Fuzzy Clustering Technique for Color Image Segmentation in terms of PSNR and Accuracy

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Ritu Agrawal
Prof. Manisha Sharma


Segmentation of an image refers to the division of pixels into homogeneous classes or clusters so that items in the same class are as similar as possible and items in different classes are as dissimilar as possible. The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Since there are more than 16 million colors available in any given image and it is difficult to analyses the image on all of its colors, the likely colors are grouped together by image segmentation. The clustering techniques for image segmentation using Optimal Fuzzy C means (OFCM,) and K means algorithm are being analyzed. The objective of the paper is to compare the performance of clustering techniques for color images using the Peak signal to Noise ratio (PSNR) and Accuracy.


Keywords: Segmentation, Fuzzy Clustering, OFCM, K-means clustering, segmentation Accuracy, PSNR


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