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ACCV
2010
Springer

Descriptor Learning Based on Fisher Separation Criterion for Texture Classification

9 years 4 months ago
Descriptor Learning Based on Fisher Separation Criterion for Texture Classification
Abstract. This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intraclass similarity and inter-class distance. Image structures are thus be described by a new FSC-based learning (FBL) encoding method. Unlike previous handcraft-design encoding methods, such as the LBP and SIFT, supervised learning approach is used to learn an encoder from training samples. We find that such a learning technique can largely improve the discriminative ability and automatically achieve a good tradeoff between discriminative power and efficiency. The commonly used texture descriptor: local binary pattern (LBP) is taken as an example in the paper, so that we then proposed the FBL-LBP descriptor. We benchmark its performance by classifying textures present in the Outex TC 0012 database for rotation...
Yimo Guo, Guoying Zhao, Matti Pietikäinen, Zh
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where ACCV
Authors Yimo Guo, Guoying Zhao, Matti Pietikäinen, Zhengguang Xu
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