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

doi:10.1109/LSP.2022.3158199

2022-01-01

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DOI：10.1109/LSP.2022.3158199

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摘要

En 中文

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 ($)over-right-arrowt(P) with different directional values of all magnitude vectors to replace the scalar threshold/ 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 CJIBP, the classification accuracy and the robustness to noise of these methods can be significantly improved.

Keyword:

Local binary pattern (LBP)

adaptively binarizing magnitude vector (ABMV)

threshold

average vector threshold

texture classification