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Subspace learning for Mumford–Shah-model-based texture segmentation through texture patches |
Applied Optics, Vol. 50, Issue 21, pp. 3947-3957 (2011)
http://dx.doi.org/10.1364/AO.50.003947
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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.
© 2011 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
History
Original Manuscript: December 9, 2010
Revised Manuscript: April 25, 2011
Manuscript Accepted: April 30, 2011
Published: July 13, 2011
Citation
Yan Nei Law, Hwee Kuan Lee, and Andy M. Yip, "Subspace learning for Mumford–Shah-model-based texture segmentation through texture patches," Appl. Opt. 50, 3947-3957 (2011)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-50-21-3947
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