Abstract:

On the basis of sparse subspace clustering algorithm, a novel image segmentation method based on weightedsparse subspace clustering is presented. By the constraints of weightedsparsity, each feature data can be linearly represented by a few most similar feature data within the same subspace, and the resulting coefficient matrix sparse interclass. Experiments show that the proposed weightedsparse subspace clustering method can obtain higher clustering accuracy than the state of art methods for both clean and noisy data. Segmentation results by using this method on natural color images show good visual consistency.