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This paper presents a new stereo matching algorithm based on inter-regional cooperative optimization. The proposed algorithm uses regions as matching primitives and defines the corresponding region energy functional for matching by utilizing the color statistics of regions and the constraints on smoothness and occlusion between adjacent regions. In order to obtain a more reasonable disparity map, a cooperative optimization procedure has been employed to minimize the matching costs of all regions by introducing the cooperative and competitive mechanism between regions. Firstly, a color based segmentation method is used to segment the reference image into regions with homogeneous color. Secondly, a local window-based matching method is used to determine the initial disparity estimate of each image pixel. And then, a voting based plane fitting technique is applied to obtain the parameters of disparity plane corresponding to each image region. Finally, the disparity plane parameters of all regions are iteratively optimized by an inter-regional cooperative optimization procedure until a reasonable disparity map is obtained. The experimental results on Middlebury test set and real stereo images indicate that the performance of our method is competitive with the best stereo matching algorithms and the disparity maps recovered are close to the ground truth data.