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Recently, deep Convolutional Neural Networks (CNN) have demonstrated strongperformance on RGB salient object detection. Although, depth information canhelp improve detection results, the exploration of CNNs for RGB-D salientobject detection remains limited. Here we propose a novel deep CNN architecturefor RGB-D salient object detection that exploits high-level, mid-level, and lowlevel features. Further, we present novel depth features that capture the ideasof background enclosure and depth contrast that are suitable for a learnedapproach. We show improved results compared to state-of-the-art RGB-D salientobject detection methods. We also show that the low-level and mid-level depthfeatures both contribute to improvements in the results. Especially, F-Score ofour method is 0.848 on RGBD1000 dataset, which is 10.7% better than the secondplace.

Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features