Supervised texture segmentation using the subspace mumford-shah model

Abstract

We propose a novel image segmentation model, called the Subspace Mumford-Shah model, which incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms standard Mumford-Shah models since it can separate textures which are less separated in the full feature space. The method also has an increased robustness and convergence speed compared to existing subspace clustering methods. Experimental results are presented to confirm the usefulness of subspace clustering in texture segmentation.