Journal article Open Access
Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia
Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different applications. There are different methods and one of the most popular methods is k-means clustering algorithm. K-means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. Enhanced k-means clustering is used to improve accuracy and efficiency of k means clustering algorithm. The number of clusters is changed for fuzzy c-mean algorithm. Subtractive cluster is used to generate the initial centers and these centers are used in k-means algorithm for the segmentation of image. Genetic algorithm is used for centroids in the given value K clusters (GAKM). GAKM is good for complex problems it retains best features. An outcome revealed that the accuracy and performance of GAKM is better than simple K-means and other clustering algorithms.