A Fast Mean Shift Procedure with New Iteration Strategy and Re-sampling

Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly and is not practical. In this paper, an improved version of mean shift algorithm is proposed and implemented. The fast mean shift procedure uses a new iteration strategy and re-sampling. The new iteration strategy is based on updating cluster centers according to dynamically updated sample set. And the original data set is simplified by re-sampling, which accelerates the algorithm more significantly. Experimental results demonstrate the efficiency of the fast mean shift procedure in clustering problems.