A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification

Local Binary Pattern (LBP) based framework only uses a scalar threshold to binarize all magnitude vectors in P different directions around each center pixel of a texture image. Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in P different directions around each center pixel. Furthermore, the value of magnitude vectors can have dramatic changes from coarse areas to flat areas in the same texture image. Therefore, using a scalar threshold calculated from whole texture image can not precisely binarize all magnitude vectors in coarse areas and flat areas simultaneously. To overcome these two drawbacks, we propose a novel adaptively binarizing magnitude vector (ABMV) method. Firstly, we adaptively calculate the average vector threshold $\boldsymbol{\vec{t}_{P}}$ with P different directional values of all magnitude vectors to replace the scalar threshold t to binarize the magnitude vectors. The proposed ABMV method can more precisely extract the different magnitude features in P different directions around each center pixel. Secondly, we divide the original texture image into smaller sub-images and adaptively extract their average vector threshold from each sub-image separately. Because the correlation of the pixels in the same sub-image is stronger than that in a whole texture image, the ABMV method can more precisely extract different magnitude features from either coarse areas or flat areas. Finally, we introduce the proposed ABMV method into LBP-based framework. Extensive experiments are conducted on five representative texture databases: Outex, UIUC, CUReT, XU_HR and ALOT database. After introducing the ABMV method into CLBP, CLBC, BRINT and CJLBP, the classification accuracy and the robustness to noise of these methods can be significantly improved.