Abstract

　　The mean shift is a kernel-type weighted mean procedure that can be used to find the mode of density estimation. We will introduce three classes of Gaussian, Cauchy and generalized Epanechnikov kernels with their shadows. The robust properties of the mean shift based on these three kernels are also investigated. We use a graphical method of correlation comparisons as an estimation of defined stabilization parameters. The graphical method can solve these bandwidth selection problems from a different point of view. Some numerical examples and comparisons demonstrate the superiority of the proposed method including those of computational complexity, cluster validity, and improvements of mean shift in large continuous, discrete data sets. We also show a simple example of mean shift-based clustering algorithm to image segmentation.