南京信息工程大学学报2025，Vol.17Issue(2)：227-234,8.DOI:10.13878/j.cnki.jnuist.20230921003
基于改进的LBP和Gabor滤波器的纹理特征提取方法
Texture feature extraction based on improved LBP and Gabor filter
摘要
Abstract
Texture extraction,a pivotal task in computer vision,significantly influences the accuracy of texture clas-sification.Traditional single-texture extraction methods often fail to accurately describe the characteristics of various textures.To address this issue,this paper proposes a texture extraction approach based on an Improved Position Local Binary Pattern(IPLBP)and Gabor filters.The proposed IPLBP enhances texture description capability by in-tegrating texture position information into the LBP framework.Specifically,the IPLBP algorithm captures local texture nuances,while Gabor filters extract global texture attributes.Subsequently,these two complementary feature sets are fused and classified using Support Vector Machine(SVM).Experimental results demonstrate that the pro-posed approach exhibits excellent performance in texture material classification tasks.Notably,compared to traditional LBP algorithms,the IPLBP-Gabor filter approach more accurately discerns the subtle differences between diverse texture features,thereby enhancing texture classification accuracy.关键词
纹理提取/局部二值模式/Gabor滤波器/支持向量机Key words
texture extraction/local binary pattern(LBP)/Gabor filter/support vector machine(SVM)分类
计算机与自动化引用本文复制引用
陈佳明,陈旭,任硕,邸宏伟..基于改进的LBP和Gabor滤波器的纹理特征提取方法[J].南京信息工程大学学报,2025,17(2):227-234,8.基金项目
江苏省自然科学基金(BK20170955) （BK20170955）
