基于改进的K均值聚类彩色图像分割方法

Color Image Segmentation Method Based on an Improved K-means Clustering Algorithm

提出了一种改进的K均值聚类图像分割方法。针对彩色图像的像素特征，利用Ohta等人的研究成果，选取能有效表示彩色像素特征的彩色特征集中的第一个分量作为图像像素的一维特征向量，用来替代经典K均值聚类图像分割中的灰度．大大降低了运算量。基于粗糙集理论的算法，求出初始聚类个数与均值。选用对特征空间结构没有特殊要求的特征距离代替欧氏距离，应用改进的K均值聚类算法对样本数据进行聚类，从而实现对彩色图像的快速自动分割。实验表明，该图像分割算法可有效提高图像分类的精度和准确度，并且运算代价小．收敛速度快。

A new image segmentation method based on an improved K-means clustering algorithm is proposed in this paper. To reduce the computational cost, the first component of color feature set discovered by Ohta et al. is chosen as the one-dimensional eigenvector. It is used as the image gray in the image segmentation method employing the classic K-means clustering method. Applying the algorithm based on the rough set theory, the number and the centroids of the clusters are obtained, which initialize the kernel K-means clustering. Feature distance, which is suitable for any structure of eigenvector space, is used instead of Euclidian distance to overcome the influence caused by the structure of eigenvector space. Then an improved K-means clustering algorithm is introduced to cluster the sample data. Experimental results show that the presented image segmentation method can effectively improve the precision and accuracy of image segmentation, and has small computational cost and fast convergence speed.