Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
This work addresses the problem of improving object detection accuracy in RGB-D images for computer vision applications, representing a strong specific gain in a domain-specific area.
The paper tackles RGB-D salient object detection by proposing a novel CNN architecture that incorporates background enclosure and depth contrast features, achieving a 10.7% improvement in F-Score over the second-best method on the RGBD1000 dataset.
Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection. Although, depth information can help improve detection results, 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 exploits high-level, mid-level, and low level features. Further, we present novel depth features that capture the ideas of background enclosure and depth contrast that are 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 the results. Especially, F-Score of our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second place.