Description

Our Proposed work describes an evolutionary approach for unsupervised gray-scale image segmentation that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In our proposed work fuzzy c-means clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiplefeature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.