The human visual system is capable of rapid response, even in the presence of massive quantities of visual information. This is possible because it restricts the operation of further processing stages to a small, potentially important, subset of the incoming information. This mechanism is called visual attention and is drawn by distinctive, visually salient, regions of the scene. Detection of visually salient regions is widely employed in vision-based applications, since a reduction in visual search space can lead to significant improvement in computational performance. Despite recent advances in salient region detection, most efforts have focused on improving accuracy, at the expense of increased execution time, significantly hindering their applicability. To address this, a fast and accurate salient region detection method is presented in this work, based on an efficient saliency estimate called random color distance map. This estimate is joint upsampled into an accurate saliency map, which is assessed and compared to saliency maps obtained by other four state-of-the-art methods on the MSRA1K, MSRA10K and SED2 datasets, showing that it is highly competitive in both accuracy and execution time.