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Parametric Free Kernel Based Fuzzy C Means Algorithm in Color Image Segmentation

Tripty Singh, Jibu Varghese K, G. Aravind, Glence Joseph, H M Andan


In this paper, design and implement an algorithm for parametric free kernel based fuzzy c means in color image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The fuzzy factor depends on both the local information and gray level information together. So new algorithm can select the most appropriate cluster after finding the membership values. For finding the performance to noise, one more term is added that is kernel parameter and it is parametric free. An application that is useful for color images is developed and implemented using parametric free kernel based fuzzy C means segmentation algorithm. The results showed that the parametric free kernel based fuzzy C means algorithm segmented in such a way as we obtained in gray scale. But if we use low resolution pixel color images, it is very hard to identify the segmented part. This whole algorithm is depending on one parameter that is segmentation accuracy. Segmentation accuracy and resolution of the images are directly proportional. That means resolution of the image increases, segmentation accuracy also increases. If we look through the proposed algorithm, the loop will execute in 3 times. Because the color image contain 3 components, red, green and blue. So in terms of computation time, the algorithm is less efficient.

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