Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9300
Title: Texture classification via patch-based sparse texton learning
Authors: Xie, J
Zhang, L 
You, J 
Zhang, D 
Keywords: K-means
Sparse representation
Texton
Texture classification
Issue Date: 2010
Source: Proceedings - International Conference on Image Processing, ICIP, 2010, p. 2737-2740 How to cite?
Abstract: Texture classification is a classical yet still active topic in computer vision and pattern recognition. Recently, several new texture classification approaches by modeling texture images as distributions over a set of textons have been proposed. These textons are learned as the cluster centers in the image patch feature space using the K-means clustering algorithm. However, the Euclidian distance based the K-means clustering process may not be able to well characterize the intrinsic feature space of texture textons, which if often embedded into a low dimensional manifold. Inspired by the great success of l1-norm minimization based sparse representation (SR), in this paper we propose a novel texture classification method via patch-based sparse texton learning. Specifically, the dictionary of textons is learned by applying SR to image patches in the training dataset. The SR coefficients of the test images over the dictionary are used to construct the histograms for texture classification. Experimental results on benchmark database validate the effectiveness of the proposed method.
Description: 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010
URI: http://hdl.handle.net/10397/9300
ISBN: 9781424479948
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5651387
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