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.
Compared to RGB salient object detection, fewer methods use RGB-D values for computing saliency. Peng al calculate a saliency map by combining low, middle, and high level saliency information @cite_19 . Ren al calculate region contrast and use background, depth, and orientation priors. They then produce a saliency map by applying PageRank and a MRF to the outputs @cite_27 . Ju al calculate the saliency score using anisotropic center-surround difference and produce a saliency map by refining the score applying Grabcut segmentation and a 2D Gaussian filter @cite_11 . Feng al improve RGB-D salient object detection results based on the idea that salient objects are more likely to be in front of their surroundings for a large number of directions @cite_15 . All the existing RGB-D methods use hand-crafted parameters, such as for scale and weights between metrics. However, real world scenes contain unpredictable object arrangements for which fixed hand coded parameters may limit generalization. No published papers have yet presented a CNN architecture for RGB-D salient object detection. A preliminary paper (Arxiv only) uses only low-level color and depth features @cite_28 .
@cite_28 @cite_19 @cite_27 @cite_15 @cite_11
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Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection. In contrast to existing works, in which raw image pixels are fed directly to the CNN, the proposed method takes advantage of the knowledge obtained in traditional saliency detection by adopting various flexible and interpretable saliency feature vectors as inputs. This guides the CNN to learn a combination of existing features to predict saliency more effectively, which presents a less complex problem than operating on the pixels directly. We then integrate a superpixel-based Laplacian propagation framework with the trained CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three data sets demonstrate that the proposed method consistently outperforms the state-of-the-art methods. Inspired by the effectiveness of global priors for 2D saliency analysis, this paper aims to explore those particular to RGB-D data. To this end, we propose two priors, which are the normalized depth prior and the global-context surface orientation prior, and formulate them in the forms simple for computation. A two-stage RGB-D salient object detection framework is presented. It first integrates the region contrast, together with the background, depth, and orientation priors to achieve a saliency map. Then, a saliency restoration scheme is proposed, which integrates the PageRank algorithm for sampling high confident regions and recovers saliency for those ambiguous. The saliency map is thus reconstructed and refined globally. We conduct comparative experiments on two publicly available RGB-D datasets. Experimental results show that our approach consistently outperforms other state-of-the-art algorithms on both datasets. Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate objectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate objectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. We propose an object-level salient detection algorithm which explicitly explores bottom-up visual attention and objectness cues.Some category-independent object candidates are firstly segmented from the image by the quantized color attributes of images.Global cues and candidate objectness are developed, to evaluate bottom-up visual attention of the whole image and the objectness of the object candidates respectively.By explicitly combining local objectness cue with global low-level saliency cues with candidates location and color attributes, our proposed method is more suitable for processing images with complex background. To facilitate efficiency, most recent successful saliency detection methods are built on superpixel level. However, saliency detection with single-scale superpixel segmentation may fail in capturing the intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize more reliable saliency detection, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank analysis. Specifically, we construct a multiscale superpixel pyramid and derive the corresponding saliency map using multiple saliency features and priors for each single scale at first. Then, we show that by joint low-rank analysis of multiscale saliency maps, we can obtain a more reliable adaptively fused saliency map that takes all scales saliency results into account. We further propose a GMM-based co-saliency prior to enable the above approach to detecting co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed approach over state-of-the-art methods.