Applying New Method for Computing Initial Centers of k-Means Clustering with Color Image Segmentation

As a classic clustering method, the traditional k-Means algorithm has been widely used in image processing and computer vision, pattern recognition and machine learning. It is known that the performance of the k-means clustering algorithm depends highly on initial cluster centers. Generally initial cluster centers are selected randomly, so the algorithm could not lead to the unique result. In this paper, we present a method to compute initial centers for k-means clustering. Our method based on an efficient technique for estimating the modes of a distribution. We apply the new method in segmentation phase of color images. The experimental results appeared quite satisfactory.