基于RB-K平均带宽设定的Adaptive Mean shift

Meanshift是一个用在图像滤波、图像分割中的迭代过程。对于迭代过程，带宽的设定很重要。固定带宽的Meanshift方法对于输入数据适应能力差，而现有可变带宽估计方法有的对于固定带宽的方法改进并不明显，有的时间复杂度较高，因此对现有带宽估计方法做了改进，提出了一种新的带宽估计方法，即采用K平均聚类算法进行带宽估计，并通过实验证实了该方法的有效性。

RB-K-means Based Adaptive Mean shift

Mean shift is an effective iterative procedure that has been used in edge preserving filtering and image segmentation. It is critical to set bandwidth properly for Iteration. However, traditional fixed bandwidth mean shift is sensitive to input data, and current variable bandwidth estimation methods are not popularly accepted for their high time complexity or inconvenience. Here a new bandwidth estimate method has been proposed, namdy, K-means is employed to estimate bandwidth for each sub-region and its effectiveness is indicated by experiments.