title: Subspace learning for Mumford-Shah-model-based texture segmentation through texture patches.
abstract: In this paper, we develop a robust and effective algorithm for texture segmentation and feature selection. The approach is to incorporate a patch-based subspace learning technique into the subspace Mumford-Shah (SMS) model to make the minimization of the SMS model robust and accurate. The proposed method is fully unsupervised in that it removes the need to specify training data, which is required by existing methods for the same model. We further propose a novel (to our knowledge) pairwise dissimilarity measure for pixels. Its novelty lies in the use of the relevance scores of the features of each pixel to improve its discriminating power. Some superior results are obtained compared to existing unsupervised algorithms, which do not use a subspace approach. This confirms the usefulness of the subspace approach and the proposed unsupervised algorithm.
authors.affiliation: Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore. lawyn@bii.a‐star.edu.sg
authors.forename: Yan Nei
authors.initials: YN
authors.lastname: Law
authors.forename: Hwee Kuan
authors.initials: HK
authors.lastname: Lee
authors.forename: Andy M
authors.initials: AM
authors.lastname: Yip
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D055620:Optical Phenomena; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*