Image Segmentation - K-means Clustering
Introduction to K-means clustering-algorithm The K-means clustering algorithm comprised of three steps, they distance, minimum-distance cluster assignment and cluster centroid update. These three steps are repeatedly executed until convergence meet or number of iteration end. The K-means algorithm splits the given dataset (image) into K number of clusters or groups It assigns a member(pixel) into a cluster (group) based on minimum distance between the pixel and all cluster centroids The algorithm is not complex and iterative procedure steps The high speed convergence but stayed on local minimum at most of times Unsupervised Clustering The K-means algorithm has no training phase. The dataset (image pixels) to be clustered is not attached with class or target variables. Assumed K number of C...