Abstract: For real-time scene feature extraction and matching, conventional binary descriptors improve the speed of the descriptor generation and matching procedure while the false matching rate is high, such as Binary Robust Independent Elementary Features (BRIEF) which is only based on pixel intensity comparisons. To solve this problem, an improved binary descriptor was proposed in this paper, which preserved not only the pixel intensity information, but also the local texture information based on the gradient value. Additionally, the orientation of the centroid vector was also used in the descriptor calculation process, so that the binary descriptors were orientation-invariant. Image Sequences dataset was used to evaluate the performance of the proposed method, and the average matching accuracy rate of the proposed method was 44.59%, higher than that of the BRIEF algorithm. Experimental results show that the proposed descriptors have high accuracy and robustness when dealing with image rotation and scale transformation.