基于小波变换和kd树聚类的快速纹理分割算法

提出了一种基于小波变换和k均值聚类的快速纹理图像分割算法。该方法包括特征提取、特征平滑、纹理分割三个阶段。其中，特征提取在金字塔结构小波变换的基础上进行；特征平滑利用一种四分法来完成特征图像的噪声平滑和边缘保持；纹理分割则利用kd树作为数据结构来运行k均值聚类算法从而实现纹理图像的快速分割。实验结果表明与直接的k均值聚类算法相比，该方法在运行时间上得到了明显的提高。

Fast texture segmentation algorithm based on wavelet transform and kd-tree clustering

A texture image segmentation algorithm based on wavelet transform and kd-tree clustering was studied in this paper. Firstly, texture features of an image were extracted using wavelet transform. Secondly, an improved algorithm based on quarter partition was given to smooth the texture feature image. Thirdly, the clustering algorithm using the kd-tree data structure was applied to the texture segmentation, and then a fast texture feature clustering effect was achieved. At last, simulations were performed on the presented algorithm, and the simulation result showed that the presented algorithm has lower segmentation error rate, higher accuracy and better in-time performance.