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...

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Yimo Guo, Guoying Zhao, Matti Pietikäinen, Zhengguang Xu
• Keywords: ACCV 2010, Computer Vision, Image Descriptor, Texture Classification, Texture Descriptor