Saliency detection is a fundamental part of computer vision applications. Its goal is to obtain a high-quality saliency map that can detect important pixels or regions in an image

which attracts human visual attention the most. Recently

saliency detection approaches in RGB-D images have become increasingly popular

and depth information is proven as a fundamental element of human vision. Most existing saliency detection methods are concentrated on detecting salient objects in 2D images

but they can not be used in detecting salient objects in RGB-D images. In this paper

however

a new RGB-D saliency detection approach based on feature integration and saliency-depth(S-D) probability correction method

is proposed. The proposed method considers image features both at the 2D and RGB-D levels

and extracts color and depth features to complement each other. First

the method extracts color and depth features

and sets four boundaries as background seeds to compute the initial saliency map by manifold-ranking algorithm. Second

according to RGB image saliency and depth maps

the method computes S-D correction probability. Third

the method computes another saliency depth map and uses the S-D correction probability to correct the result. After correction

the proposed method finally selects foreground seeds through image threshold processing. Then

a final saliency map is optimized by using the manifold-ranking algorithm again. In our experiments

we evaluate the saliency detection ability of our method and six state-of-the-art methods on a large and prevalent RGB-D image dataset

which contains 1

000 images. Experimental results indicate that saliency detection results from our proposed method are much closer to the ground truth than other methods. We also plot a precision-recall curve to show the advantages of the proposed method. From the precision-recall curve

we can conclude that the proposed method has better performance than the other five methods when recall is the same. In addition

we evaluate the time complexity of our algorithm. Our method can process a single image in 2.150 seconds

which is faster than the speed of most of the other methods. In this paper

we propose a novel RGB-D saliency detection approach that combines color features from the RGB image and depth features from the depth image. The depth features are extracted to guide RGB image saliency ranking. The RGB saliency detection results are utilized for saliency detection alignment results of depth images. Experiment results demonstrate that the manifold-ranking approach with feature integration can fuse depth and color features effectively

and enable those two components to complement each other. With the help of S-D probability correction

RGB saliency detection results can effectively guide depth saliency detection.