Fast dynamic Gaussian mean-shift algorithm based on adaptive bandwidth
基于自适应带宽的快速动态高斯核均值漂移算法

The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate.We propose a dynamic mean-shift algorithm based on adaptive bandwidth.The number of data sets is reduced by adaptive space discretization;the convergence rate is improved by dynamically updating the data set,and the efficiency is promoted by replacing the overlapping points with a special point in the iterations.The anisotropic bandwidth is updated according to the diameter of the data set.Experiments validate the improvement of the convergence rate of Gaussian mean-shift with lower complexity in computation.