Descriptor Learning Based on Fisher Separation Criterion for Texture Classification

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 intra-class 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 invariant texture classification, KTH-TIPS2 database for material categorization and Columbia-Utrecht (CUReT) database for classification under different views and illuminations. The promising results verify its robustness to image rotation, illumination changes and noise. Furthermore, to validate the generalization to other problems, we extend the application also to face recognition and evaluate the proposed FBL descriptor on the FERET face database. The inspiring results show that this descriptor is highly discriminative.