Error Based Nystrom Spectral Clustering Image Segmentation

Spectral clustering algorithm has been a research hotspot in the field of image processing, recent years. Spectral clustering based on the similarity of data while structure of similarity matrix is complex. The calculation of spectral clustering can be very time-consuming, especially in the process of Eigen-decomposition for Laplacian matrix. Nystrom extension method could obtain the approximation solution of eigenvectors by using a small amount of sample information, reduce the computational complexity of spectral clustering effectively. Based on the features of image and the error analysis of Nystrom a new sampling method is presented. Using Uniform Sampling generates a set of cluster centers at first; then, minimize the error between data and centers by iteration; finally, typical experiment results and analysis are given.