Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104638
Title: Supervised texture segmentation using the subspace mumford-shah model
Authors: Law, Y.N.
Lee, H.K.
Yip, A.M. 
Keywords: Gabor transform
Image segmentation
Mumford-Shah segmentation model
Subspace clustering
Texture segmentation
Issue Date: 2009
Citation: Law, Y.N.,Lee, H.K.,Yip, A.M. (2009). Supervised texture segmentation using the subspace mumford-shah model. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 2 : 554-560. ScholarBank@NUS Repository.
Abstract: We propose a novel image segmentation model, called the Subspace Mumford-Shah model, which incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then 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. The method also has an increased robustness and convergence speed compared to existing subspace clustering methods. Experimental results are presented to confirm the usefulness of subspace clustering in texture segmentation.
Source Title: Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/104638
ISBN: 9781601321190
Appears in Collections:Staff Publications

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