Superpixel based color contrast and color distribution driven salient object detection

• A comprehensive saliency detection system is proposed, which combines the color contrast and color distribution in a unified manner.
• Additional stages such as global saliency smoothing and meanshift segmentation refinement are introduced, which improve the performance.
• Our method outperforms the state-of-the-art on both qualitative and quantitative evaluation.
• The results on other applications like content-aware image resizing also show the superiority of our method.

Color is the most informative low-level feature and might convey tremendous saliency information of a given image. Unfortunately, color feature is seldom fully exploited in the previous saliency models. Motivated by the three basic disciplines of a salient object which are respectively center distribution prior, high color contrast to surroundings and compact color distribution, in this paper, we design a comprehensive salient object detection system which takes the advantages of color contrast together with color distribution and outputs high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, and final refinement.In color contrast saliency computation, we calculate center-surrounded color contrast and then employ the distribution prior in order to select correct color components. A global saliency smoothing procedure that is based on superpixel regions is introduced as well. This processing step preferably alleviates the saliency distortion problem, leading to the entire object being highlighted uniformly. Finally, a saliency refinement approach is adopted to eliminate artifacts and recover unconnected parts within the combined saliency maps.In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutter. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets. Excellent content-aware image resizing also could be achieved using our saliency maps.