Effective algorithm for determining the number of clusters and its application in image segmentation

The k-means algorithm is a popular clustering method for image segmentation. However, the main disadvantage of this algorithm is its dependence on the number of initial clusters. In this paper, we present an optimal criterion which can select the best segmentation result with less number of clusters. The optimal criterion overcomes the shortcoming of initialization based on the intra-class and inter-class difference. Eight digital images were employed to verify the segmentation results of the optimal criterion. Simultaneously, we have improved the traditional k-means algorithm to find the initial clustering centers efficiently. Experimental results show that the segmented images selected by the optimal criterion have sufficient stability and robustness. In addition, we verify the consistency of results by two kinds of objective assessment measures. The proposed optimal criterion can successfully display the best segmentation results precisely and efficiently so as to instead of artificial selection.