Texture extraction has always been a crucial task in the field of computer vision, and the quality of texture extraction often has a critical impact on the accuracy of texture classification. Traditional single texture extraction methods struggle to accurately describe the characteristics of various textures. Therefore, this paper proposes a texture extraction algorithm based on an improved Position Local Binary Patterns (IPLBP) and Gabor filters. The improved algorithm enhances the texture description capability by extracting texture position information based on the Local Binary Patterns (LBP). In this paper, the improved algorithm is used to extract local texture information, while Gabor filters are employed to extract global texture information. The two types of feature information are then fused and classified using Support Vector Machines(SVM).Experimental results demonstrate that the proposed algorithm exhibits excellent performance on texture material classification tasks. Compared to traditional LBP algorithms, this algorithm can more accurately capture the differences between different texture features.