LBP Texture Classification Based on Multi-resolution of Gaussian-Hermite Moments

doi:10.3969/j.issn.1006-2475.2016.09.015

Abstract

Abstract: LBP（Local Binary Pattern)as a texture descriptor to capture images of local texture information in a single resolution, a method based on multi-resolution of the Gaussian-Hermite moments and LBP texture classification is proposed. Firstly, with the multi-resolution characteristic of image texture, the multi-resolution of the Gaussian-Hermite moments is used to reconstruct image. Then LBP is used for the feature extraction of reconstruction image. Finally, the K-nearest neighbor feature space distance method is used to classify image texture. KTH-TIPS texture library images are selected for texture classification test, the experimental results show that compared with the traditional LBP texture classification method, the method based on multi-resolution of the Gaussian-Hermite moments and local binary pattern texture classification is used to texture classification, which make more comprehensive to describe the image texture information to get higher classification accuracy.

Key words: LBP, multi-resolution, Gaussian-Hermite moments, image reconstruction, texture classification

CLC Number:

TN911.73

ZHANG Jing-jing. LBP Texture Classification Based on Multi-resolution of Gaussian-Hermite Moments[J]. Computer and Modernization, 2016, 0(9): 68-72.

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