We propose an efficient regional histogram (RH)-based computation model for saliency detection in natural images. First, the global histogram is constructed by performing an adaptive color quantization on the original image. Then multiple RHs are built on the basis of the region segmentation result, and the color-spatial similarity between each pixel and each RH is calculated accordingly. Two efficient measures, distinctiveness and compactness of each RH, are evaluated based on the color difference with the global histogram and the color distribution over the whole image, respectively. Finally, the pixel-level saliency map is generated by integrating the color-spatial similarity measures with the distinctiveness and compactness measures. Experimental results on a dataset containing 1000 test images with ground truths demonstrate that the proposed saliency model consistently outperforms state-of-the-art saliency models.