Please use this identifier to cite or link to this item: https://doi.org/10.1364/AO.50.003947
Title: Subspace learning for Mumford-Shah-model-based texture segmentation through texture patches
Authors: Law, Y.N.
Lee, H.K.
Yip, A.M. 
Issue Date: 20-Jul-2011
Citation: Law, Y.N., Lee, H.K., Yip, A.M. (2011-07-20). Subspace learning for Mumford-Shah-model-based texture segmentation through texture patches. Applied Optics 50 (21) : 3947-3957. ScholarBank@NUS Repository. https://doi.org/10.1364/AO.50.003947
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.
Source Title: Applied Optics
URI: http://scholarbank.nus.edu.sg/handle/10635/104215
ISSN: 1559128X
DOI: 10.1364/AO.50.003947
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