Pyramid-based multi-structure local binary pattern for texture classification

Authors:
Yonggang He;Nong Sang;Changxin Gao
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

Citing 13
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Abstract

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of local binary pattern. In this paper, we investigate the drawback of conventional LBP operators in describing some textures that has the same small structures but differential large structures. And a multi-structure local binary pattern operator is achieved by executing the LBP method on different layers of image pyramid. The proposed method is simple yet efficient to extract not only the micro structures but also the macro structures of texture images. We demonstrate the performance of our method on the task of rotation invariant texture classification. The experimental results on Outex database show advantages of the proposed method.