Selective features for RGB-D saliency

The depth image has greatly broadened various applications of computer vision, however, it is seldom explored in the field of salient object detection. In this paper, we propose a learning-based approach for extracting saliency from RGB-D images. For best fitting the contrast-based stimulus that guides the saliency search in human vision system, massive visual attributes that are extracted from several multi-scale feature channels such as the color channels, the texture channels and the depth channel are investigated to represent the contrasts between segments. In addition, discriminative features are able to be automatically selected by learning several decision trees based on the ground truth, and those features are further utilized to search the saliency regions via voting the predictions of the trees. We argue that, introducing the selective features from the depth information of a scene can benefit the saliency detection and achieve better performance than only using the appearance features that are extracted from the color images of the same scene. The experimental results demonstrate that our method outperforms other 10 state-of-the-art approaches on a large RGB-D benchmark.