In this paper, we propose an efficient saliency model using regional color and spatial information. The original image is first segmented into a set of regions using a superpixel segmentation algorithm. For each region, its color saliency is evaluated based on the color similarity measures with other regions, and its spatial saliency is evaluated based on its color distribution and spatial position. The final saliency map is generated by combining color saliency measures and spatial saliency measures of regions. Experimental results on a public dataset containing 1000 images demonstrate that our computationally efficient saliency model outperforms the other six state-of-the-art models on saliency detection performance.