Enhanced moving K-means (EMKM) algorithm for image segmentation

As of now, numerous improvements have been carried out to increase the performance of previous existing algorithms for image segmentation with the limitation lying on the intra clustering variance. However, most of them tend to have met with inadequate results. This paper presents an improved version of the Moving KMeans algorithm called Enhanced Moving K-Means (EMKM) algorithm. In the proposed EMKM, the moving concept of the conventional Moving K-Means (i.e. certain members of the cluster with the highest fitness value are forced to become the members of the clusters with the smallest fitness value) is enhanced. Two versions of EMKM, namely EMKM-1and EMKM-2 are proposed. The qualitative and quantitative analyses have been performed to measure the efficiency of both EMKM algorithms over the conventional algorithms (i.e. K-Means, Moving KMeans, and Fuzzy C-Means) and the latest clustering algorithms (i.e. AMKM and AFMKM). It is investigated that the proposed algorithms significantly outperform the other conventional clustering algorithms.