Please use this identifier to cite or link to this item:
|Title:||Semi-supervised subspace learning for Mumford-Shah model based texture segmentation|
|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. https://doi.org/10.1364/OE.18.004434|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 19, 2018
WEB OF SCIENCETM
checked on Oct 2, 2018
checked on Sep 28, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.