Evaluating the performance of Fuzzy Clustering using different distance metrics for Image Segmentation

Segmentation in image processing is an important part to analyze an image automatically. Object detection and recognition in images are done with the help of segmentation process. This paper evaluates the performance of Fuzzy Clustering method for Image Segmentation using different distance metrics namely Euclidean, Canberra, Chebyshev. The performance is tested using two digital images and is quantitatively accessed using four metrics namely Partition Entropy ($V_{par.entr.}$), Partition Coefficient ($V_{par.coef.}$), Fukuyama-Sugeno ($V_{fuku.sugn.}$) and XieBeni function ($V_{xie.ben.}$).