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Local Binary Pattern histogram based Texton learning for texture classification

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3 Author(s)
Yonggang He ; Inst. for Pattern Recognition & Artificial Intell., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Nong Sang ; Rui Huang

Local Binary Pattern (LBP) and Texton are both widely used texture analysis techniques. In this paper we propose a patch-based texture classification method that takes advantage of both LBP and Texton. Unlike the traditional LBP methods that describe a texture with the occurrence of local binary patterns in the entire image, we compute the LBP histogram in a small region around each pixel to capture the local structure information. The texton learning method is then per- formed on these LBP histograms, resulting in a texture classification algorithm that outperforms the traditional LBP-based methods due to its preservation of local structure information. It also outperforms the traditional filtering-based texton methods due to its robustness to orientation and illumination. Experimental results on two benchmark databases validate the advantages of the proposed method.

Published in:

Image Processing (ICIP), 2011 18th IEEE International Conference on

Date of Conference:

11-14 Sept. 2011