Learning RGB D Salient Object Detection Using Background Enclosure, Depth Contrast, and Top Down Features
In human visual saliency, top down and bottom up information are combined as a basis of visual attention. Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection, providing an effective mechanism for combining top down semantic information with low level features. Although depth information has been shown to be important for human perception of salient objects, the use of top down information and the exploration of CNNs for RGB D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB D salient object detection that utilizes both top down and bottom up cues. In order to produce such an architecture, we present novel depth features that capture the ideas of background enclosure, depth contrast and histogram distance in a manner that is suitable for a learned approach. We show improved results compared to state of the art RGB D salient object detection methods. We also show that the low level and mid level depth features both contribute to improvements in results. In particular, the F Score of our method is 0.848 on RGBD1000, which is 10.7% better than the current best.
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)