A Color Image Segmentation Approach Based on Improved Spectral Clustering
A color image segmentation approach based on improved spectral clustering is proposed.First,the weighted function of a weighted undirected graph is defined by Levin′s Affinity which can better capture the feature similarity between data than the traditional Gaussian kernel function.Secondly,the graph can be embedded into a subspace that contains a part of eigenvectors through liner mapping,which keeps the structure in the original space successfully when the data is mapped into a new space.Finally,K-means clustering algorithm is used in the subspace to distribute labels for each pixel and achieve the segmentation of color images.The experiment results that compared with the related spectral clustering algorithms are provided to show the effectiveness and significance of the improved algorithm.