The texture feature extraction algorithm of Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) has characteristics of high computing feature dimension, poor rotation invariance and sensitivity to illumination change. In order to solve the issues, an improved PRICoLBP algorithm was proposed. Firstly, the coordinates of two neighboring pixels were obtained by respectively maximizing and minimizing the binary sequence of image pixels. Then, the position coordinates of co-occurred pixel points were calculated via the position coordinates of the center pixel and the two neighboring pixels. Secondly, the texture information of every image pixel was extracted through utilizing the Completed Local Binary Pattern (CLBP) algorithm. Compared with PRICoLBP, the recognition rate of the proposed method was improved respectively by the percentage points of 0.17, 0.24, 2.65, 2.39 and 2.04, on the image libraries of Brodatz, Outex(TC10, TC12), Outex(TC14), CUReT and KTH_TIPS under the same classifier. The experimental results show that the proposed algorithm has better recognition effect for the images with texture rotation variation and illumination change.