We propose a global stereo matching algorithm based on mean shift image segmentation to improve image global stereo matching for its high accuracy but large calculation. Firstly, we use the mean shift algorithm to segment the original image to get the number of homogeneous regions and their labels. When calculating matching cost, we choose proper pixels according to the segmentation region of pixels, which can improve the computation speed of matching cost. Secondly, before calculating the cost aggregation, we use the
K-means algorithm to cluster the pixels according to the number of homogeneous regions K which is obtained by the mean shift algorithm before. This can improve the accuracy and speed of stereo matching. Finally, we utilize the TRW-S belief propagation algorithm to solve the energy minimization problem. Experimental results show that compared with pure global stereo matching, the proposed algorithm can improve the stereo matching accuracy and speed obviously.