Salient object detection for RGB-D image by single stream recurrent convolution neural network

Abstract

Salient object detection for RGB-D images aims to utilize color and depth information to automatically localize objects of human interest in the scene and reduce the complexity of visual analysis. Different from existing saliency detection model with double-stream network, salient object detection by Single Stream Recurrent Convolution Neural Network(SSRCNN) is proposed. First RGBD four-channels input is fed into VGG-16 net to generate multiple level features which express the most original feature for RGB-D image. The coarse saliency map from the deepest features can detect and localize salient objects, but loss the boundaries and subtle structures. So Depth Recurrent Convolution Neural Network (DRCNN) is then applied to each level feature for rendering salient object outline from deep to shallow hierarchically and progressively. With the help of deeper level feature, original depth cue and coarse saliency map, each level feature can accurately predict the salient objects in different scales. At last all the saliency maps from each level are fused together to generate final results. Extensive quantitative and qualitative experimental evaluations on four dataset demonstrate that the proposed method outperforms most state-of-the-art methods.