Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2 p. 699
Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space
Mikael Rousson, INRIA Sophia-Antipolis
Thomas Brox, Saarland University
Rachid Deriche, INRIA Sophia-Antipolis

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211535
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Abstract
In this paper, we propose a novel and efficient approach for active unsurpervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that incorporates these features in a level set based unsupervised segmentation that adaptively takes into account their estimated statistical information inside and outside the region to segment. The approach has been tested on various textured images, and its performance is favorably compared to recent studies.
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Citation: Mikael Rousson, Thomas Brox, Rachid Deriche, "Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space," cvpr, p. 699, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003

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