Learning RGB-D Salient Object Detection Using Background Enclosure, Depth Contrast, and Top-Down Features

Abstract

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.