Image Segmentation using K-means clustering
Clustering is the process of dividing the datasets or objects into groups, consisting of similar data points. Points in the same group are as similar as possible. Points in the different groups are as dissimilar as possible. K-means clustering is one of the popular algorithms in clustering and segmentation that is used to separate objects from the surrounding background. K-means clustering treats each feature point as having a location in space. The basic K-means algorithm then arbitrarily locates, that number of cluster centers in multidimensional measurement space. Each pixel in the image is then , assigned to the cluster whose arbitrary mean vector is closest. The procedure continues until there is no significant change in the location of class mean vectors between successive iterations of the algorithms. However, the K-means algorithm is very sensitive in initial starting points. K-means generates initial cluster randomly. When random initial starting points close to the final solution, K-means has a high possibility to find out the cluster center. Otherwise, it will lead to incorrect clustering results. K-means clustering is a partitioning method. The function K-means partitions data into k mutually exclusive clusters and returns the index of the cluster to which it has assigned each observation. Each cluster in the partition is defined by its member objects and by its centroid or center.