RGB-D Saliency Detection with Multi-feature-fused Optimization

This paper proposes a three-stage method using color, joint entropy and depth to detect salient regions in an image. In the first stage, coarse saliency maps are computed through multi-feature-fused manifold ranking. However, graph-based saliency detection methods like manifold ranking often have problems of inconsistency and misdetection if some parts of the background or objects have relatively high contrast with its surrounding areas. To solve this problem and provide more robust results in varying conditions, depth information is repeatedly used to segment and refine saliency maps. In details, a self-adaptive segment method based on depth distribution is used secondly to filter the less significant areas and enhance the salient objects. At last, the saliency-depth consistency check is implemented to suppress the highlighted areas in the background and enhance the suppressed parts of objects. Qualitative and quantitative evaluation on a challenging RGB-D dataset demonstrates significant appeal and advantages of our algorithm compared with eight state-of-the-art methods.