融合均值漂移和改进的K均值聚类的颜色量化

针对经典K均值聚类算法需要事先给定量化数目和量化时间长的问题,提出了一种融合均值漂移和改进的K均值聚类的颜色量化算法。首先把图像从RGB空间转化到Munsell空间,然后依据均值漂移算法以及NBS距离与人类视觉对颜色差别的定量关系自动确定量化数目,最后以kd树作为数据结构来运行K均值算法从而实现图像的快速量化。

Color Quantization Algorithm Based on Mean Shift and Improved K-means Clustering

Aiming at the problem of giving the number of quantization in advance and poor in-time performance of the conventional k-means clustering,a color image quantization algorithm based on mean shift and improved k-means is studied in this paper.Firstly,the original RGB space is converted to Munsell space.Secondly,based on the quantitative relation of the NBS distance and the color difference of human visual together with mean shift algorithm,the initial clustering centers and number are determined automatically.At last the k-mean clustering algorithm using the kd-tree data structure is applied to the color quantization,and then,a fast color image quantization effect is achieved.