Unsupervised clustering method with optimal estimation of the number of clusters: application to image segmentation
Christophe Rosenberger, Kacem Chehdi · 2002
We propose in this communication an unsupervised clustering method called MLBG based upon the K-means algorithm. The originality of this method lies in the automatic determination of the number of clusters by calling into question an intermediate result. This method also enables to improve the different steps in the K-means algorithm. We show the efficiency of the MLBG method through some experimental results and we demonstrate the usefulness of the technique for image segmentation.