基于稀疏矩阵的谱聚类图像分割算法

摘要

在图像分割中谱聚类算法需要计算像素之间的相似度矩阵,构造数据量大,并且要对拉普拉斯矩阵进行特征分解,计算比较耗时.针对这一问题,提出了一种基于稀疏矩阵的谱聚类图像分割算法.算法结合图像特征信息在不同尺度上对谱聚类进行误差分析,设计了一种新的样本信息选取方案,并利用选取的图像信息直接创建稀疏相似度矩阵.理论分析以及图像分割实验结果表明,该算法能够有效降低谱聚类的计算复杂度,同时,提高了分割的准确性和鲁棒性.%Spectral clustering based on the similarity while the structure of similarity matrix is complex in image segmentation.The calculation of spectral clustering can be very time-consuming, especially in the process of Eigen-decomposition for Laplacian matrix.Sparse matrix could obtain the approximate solution of the similarity matrix by sing a small amount of sample information, thus, reducing the computational complexity effectively.An image segmentation algorithm based on the sparse matrix is proposed.First, error analysis of the spectral clustering is carried out in different scales.Then, a novel sampling method is presented.The sample information is used to create a sparse matrix, which can be used to substitute the similarity matrix.Typical experiment results and theoretical analysis show that the proposed algorithm can effectively reduce the complexity in calculating spectral clustering and improve the accuracy and robustness of the segmentation.