Combining intrinsic dimension and local tangent space for manifold spectral clustering image segmentation

To solve the problem of high computational complexity and the lack of local similarity information in constructing similarity matrix, an algorithm combining intrinsic dimension and local tangent space (IDLTS) for manifold spectral clustering image segmentation is proposed. Firstly, considering the manifold structure of image feature space, local linear reconstruction in manifold learning is introduced, and the local tangent spatial similarity of image data is obtained. Secondly, performing the local PCA (Pincipal Components Analysis) in the K-nearest neighbor region of data points, the relationship between intrinsic dimensions of image data is calculated. Combining it with the local tangent spatial similarity, the similarity function and its related similarity matrix of the spectral clustering can be constructed. Thirdly, by sampling points and sampling points, as well as sampling points and non-sampling points, two similarity matrices are constructed with Nystrom approximation strategy and used to approximate the eigenvectors for image segmentation. Finally the IDLTS manifold spectral clustering image segmentation is accomplished based on the constructed k principal eigenvectors. Berkeley Segmentation Dataset and eight evaluation metrics are selected to compare the proposed algorithm with some existing image segmentation algorithms. Experimental results show that the IDLTS has good performance in terms of segmentation accuracy and time consumption.