Texture image segmentation on improved watershed and multiway spectral clustering

Spectral clustering is a new graph and similarity based clustering algorithm. When the image is too big, it will take a long time to compute affinity matrix and its eigenvalues and eigenvectors. In order to improve the convergent speed of spectral clustering, a two-stage texture segmentation algorithm is proposed in this paper. First, an improved watershed algorithm is used to perform pre-segmentation and then multiway spectral clustering with eigenvalue-scaled eigenvectors performs the final segmentation. This can reduce the runtime greatly and it is valuable to application with high time request. To verify the proposed algorithm, it is applied to texture image segmentation and the segmentation results are satisfying.