Local binary patterns have been widely used in texture images classification. However

conventional LBP methods focus on the distribution of LBP values and ignore the spatial contextual information between LBP patterns. In this paper

a texture images classification method based on two-dimensional Local Binary Pattern (2DLBP) is proposed. The proposed method introduces a sliding window to count the weighted occurrence number of LBP pairs on the feature map of rotation invariant uniform LBP. The radius of LBP is also changed to obtain the multi-resolution 2DLBP features. At last

texture images are classified using the methods of the support vector machine (SVM). Theoretical validation shows that the proposed method is a generalized framework

and can be integrated with other LBP variants to derive a new feature extraction method. Experimental results show that

compared with the conventional LBP

the variants of LBP

and some state of the art texture classification methods

the proposed method achieves acceptable performance in texture images classification.