摘要： 聚类作为一种有效的图像分割方法，被广泛地应用于计算机视觉领域。相较于其他聚类方法，密度峰值聚类（DPC）具有参数少且能有效识别非球形聚类的特点。基于此，引入信息论中的不确定性度量熵，提出一种改进的DPC图像分割算法。将图像像素点的颜色空间CIE Lab值作为特征数据，通过计算信息熵求得自适应截断距离以取代经验取值，建立相应的决策图并确定聚类中心总数，归类非聚类中心点，剔除噪声点从而完成图像分割。在Berkeley数据集上的实验结果表明，该算法能较好地实现彩色图像的分割，其平均分割时间和PRI指标分别为14.658 s和0.721。

关键词: 密度峰值聚类, CIE Lab颜色空间, 局部密度, 截断距离, 相对距离, 信息熵

Abstract: As an effective method of image segmentation,clustering is widely used in the field of computer vision.Compared with other clustering methods,Density Peak Clustering(DPC) has fewer parameters and can effectively identify non-spherical clustering.On this basis,this paper proposes improved DPC image segmentation algorithm by introducing the uncertainty metric entropy in information theory.The algorithm takes the CIE Lab color space values of the image pixels as feature data.By calculating the information entropy,the adaptive truncation distance is obtained to replace the empirical value.Then,the corresponding decision map is established and the total number of cluster centers is determined.Accordingly,the non-cluster center points are classified and the noise points are removed to complete image segmentation.The experimental results on the Berkeley dataset show that the algorithm can well achieve color image segmentation,and its average segmentation time and PRI index are 14.658 s and 0.721 respectively.

Key words: Density Peak Clustering(DPC), CIE Lab color space, local density, truncation distance, relative distance, information entropy

中图分类号: 

• TP391