title: Semi-supervised subspace learning for Mumford-Shah model based texture segmentation.
abstract: We propose a novel image segmentation model which incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. While the natural unsupervised approach to learn a feature subspace can easily be trapped in a local solution, we propose a novel semi-supervised optimization algorithm that makes use of information derived from both the intermediate segmentation results and the regions-of-interest (ROI) selected by the user to determine the optimal subspaces of the target regions. Meanwhile, these subspaces are 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. Experimental results are presented to confirm the usefulness of subspace clustering in texture segmentation.
authors.affiliation: Imaging Informatics Group, Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore.
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; D049832:Decapodiformes; D004717:Endometrium / Q000473:pathology; D018397:Equidae; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007858:Learning; D008962:Models, Theoretical; D009206:Myocardium / Q000648:ultrastructure; D051381:Rats