Recent cost-volume filtering-based local stereo methods have achieved comparable accuracy with global methods. However, there are still some significant outliers existing in the final disparity map. In this paper, we propose a cost-volume filtering-based local stereo matching method that employs a new combined cost and a novel secondary disparity refinement mechanism. The combined cost is formulated by a modified color census transform, truncated absolute differences of color and gradients. Symmetric guided filter is used for the cost aggregation. Different from traditional stereo matching, a novel secondary disparity refinement is proposed to further remove remaining outliers. Experimental results on Mid-dlebury benchmark show that our method ranks the 5^th out of the 144 submitted methods, and is the best cost-volume filtering-based local method. Furthermore, experiments on real world sequences also validate the effectiveness of our proposed method.