  Stereoscopic perception is an important part of human visual system that
allows the brain to perceive depth. However, depth information has not been
well explored in existing saliency detection models. In this letter, a novel
saliency detection method for stereoscopic images is proposed. Firstly, we
propose a measure to evaluate the reliability of depth map, and use it to
reduce the influence of poor depth map on saliency detection. Then, the input
image is represented as a graph, and the depth information is introduced into
graph construction. After that, a new definition of compactness using color and
depth cues is put forward to compute the compactness saliency map. In order to
compensate the detection errors of compactness saliency when the salient
regions have similar appearances with background, foreground saliency map is
calculated based on depth-refined foreground seeds selection mechanism and
multiple cues contrast. Finally, these two saliency maps are integrated into a
final saliency map through weighted-sum method according to their importance.
Experiments on two publicly available stereo datasets demonstrate that the
proposed method performs better than other 10 state-of-the-art approaches.
