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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 5 — Mar. 1, 2010
  • pp: 4434–4448

Semi-Supervised Subspace Learning for Mumford-Shah Model Based Texture Segmentation

Yan Nei Law, Hwee Kuan Lee, and Andy M. Yip  »View Author Affiliations

Optics Express, Vol. 18, Issue 5, pp. 4434-4448 (2010)

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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

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition
(100.4994) Image processing : Pattern recognition, image transforms

ToC Category:
Image Processing

Original Manuscript: December 16, 2009
Revised Manuscript: February 5, 2010
Manuscript Accepted: February 6, 2010
Published: February 18, 2010

Yan Nei Law, Hwee Kuan Lee, and Andy M. Yip, "Semi-Supervised subspace learning for Mumford-Shah model based texture segmentation," Opt. Express 18, 4434-4448 (2010)

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