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Title: Semi-supervised subspace learning for Mumford-Shah model based texture segmentation
Authors: Law, Y.N.
Lee, H.K.
Yip, A.M. 
Issue Date: 1-Mar-2010
Citation: Law, Y.N., Lee, H.K., Yip, A.M. (2010-03-01). Semi-supervised subspace learning for Mumford-Shah model based texture segmentation. Optics Express 18 (5) : 4434-4448. ScholarBank@NUS Repository.
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. © 2010 Optical Society of America.
Source Title: Optics Express
ISSN: 10944087
DOI: 10.1364/OE.18.004434
Appears in Collections:Staff Publications

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