Stereo-matching is an algorithm that through searching the corresponding relations between the projection images’ pixels of the same scene on different visual point, gains a disparity map of the scene finally. This paper presents a graph-cut-based stereo-matching algorithm using image segmentation on the basis of in-depth study on the images matching algorithms. In proposed algorithm, the reference image is divided into segments. Then modeling disparity is built inside a segment by a planar equation. A set of disparity layers is extracted from initial disparity segments in a clustering process. A global energy function is constructed. Robust minimization of the cost function is achieved by graph-cut-based optimization. The proposed algorithm produces good-quality results, especially in regions of low texture and close to disparity boundaries. The expensive computing cost for traditional global algorithms can also be reduced. Experiments demonstrate that the algorithm can meet both demands for high resolution and real time.