For the problems of needing pre-training and poor robustness to rotation and illumination changes of various improved algorithms based on local binary pattern (LBP), this paper presents a new texture classification algorithm by integrating the completed local binary pattern (CLBP) and the local geometric invariant features of the image surface. In our algorithm, the local geometric invariant features are first computed. Then the computed results are further quantified and encoded to make combination with the CLBP histogram. The proposed algorithm can ex-tract image macroscopic and microscopic features simultaneously, and it has the properties of not significantly in-creasing feature dimension, without pre-training, and invariance to the rotation and illumination changes. Experi-mental verifications are conducted on two standard texture databases, and the results demonstrate that the pro-posed algorithm outperforms the comparative classification algorithms in classification accuracy and robustness.