Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification
Alpaslan, Nuh and Hanbay, Kazim
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In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP.d The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently.d To obtain the macro- and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema.d Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes.d The magnitude information of the Hessian matrix is used in the ACS-LBP method.d A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor.d Finally, histogram concatenation is carried out.d Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC\_00013, XU HR, ALOT and STex validate the efficiency of the proposed method.d The proposed Hess-ACS-LBP method achieves about 20\% improvement over the original LBP method and 1\%-11\% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy.d Besides, the experimental results show that the proposed method achieves up to 32\% better results than the state-of-the-art deep learning based methods.d Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable.
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