Exploiting global priors for RGB-D saliency detection

Ren, Jianqiang; Gong, Xiaojin; Yu, Lu; Zhou, Wenhui; Yang, Michael Ying

doi:10.1109/cvprw.2015.7301391

Cited by 190 publications

(128 citation statements)

References 25 publications

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“…Most existing approaches for 3D saliency detection either treat the depth feature as an indicator to weight the RGB saliency map [15][16][17][18] or consider the 3D saliency map as the fusion of saliency maps of these low-level features [19][20][21][22]. It is not clear how to integrate 2D saliency features with depth-induced saliency feature in a better way, and linearly combining the saliency maps produced by these features cannot guarantee better results.…”

Section: Related Workmentioning

confidence: 99%

“…Let us compare our saliency model (BFSD) with a number of existing state-of-the-art methods, including graphbased manifold ranking (GMR) [7]; multi-context deep learning (MC) [27]; multiscale deep CNN (MDF) [28]; anisotropic centre-surround difference (ACSD) [10]; saliency detection at low-level, mid-level, and high-level stages (LMH) [19]; and exploiting global priors (GP) [20], among which GMR, MC and MDF are developed for RGB images, LMH and GP for RGB-D images, and ACSD for depth images. All of the results are produced using the public codes that are offered by the authors of the previously mentioned literature reports.…”

Section: Compared Methodsmentioning

confidence: 99%

“…Some other more complex combination algorithms have been proposed. These methods combine the depth-induced saliency map with the 2D saliency map either directly [19] or in a hierarchical way to calculate the final RGB-D saliency map [20]. However, because they are restricted by the computed saliency values, these saliency map combination methods are not able to correct incorrectly estimated salient regions.…”

Section: Related Workmentioning

confidence: 99%

“…Depth-pooling models This type of model combines depth saliency maps and traditional 2D saliency maps simply to obtain saliency maps for RGB-D images [19][20][21][22]. Peng et al provided a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency: the former one is estimated from existing RGB models, whereas the latter one is based on the multi-contextual contrast model [19].…”

Section: Related Workmentioning

confidence: 99%

“…Peng et al provided a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency: the former one is estimated from existing RGB models, whereas the latter one is based on the multi-contextual contrast model [19]. Ren et al presented a two-stage 3D salient object detection framework, which first integrates the contrast region with the background, depth and orientation priors to achieve a saliency map and then reconstructs the saliency map globally [20]. Xue et al proposed an effective visual object saliency detection model via RGB and depth cues with mutually guided manifold ranking and obtained the final result by fusing RGB and depth saliency maps [21].…”

Section: Related Workmentioning

confidence: 99%

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Visual saliency detection for RGB-D images under a Bayesian framework

Wang

Zhou

Jin

et al. 2018

IPSJ T Comput Vis Appl

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In this paper, we propose a saliency detection model for RGB-D images based on the deep features of RGB images and depth images within a Bayesian framework. By analysing 3D saliency in the case of RGB images and depth images, the class-conditional mutual information is computed for measuring the dependence of deep features extracted using a convolutional neural network; then, the posterior probability of the RGB-D saliency is formulated by applying Bayes' theorem. By assuming that deep features are Gaussian distributions, a discriminative mixed-membership naive Bayes (DMNB) model is used to calculate the final saliency map. The Gaussian distribution parameters can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm. The experimental results on RGB-D images from the NLPR dataset and NJU-DS400 dataset show that the proposed model performs better than other existing models.

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Section: Related Workmentioning

confidence: 99%

Section: Compared Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Visual saliency detection for RGB-D images under a Bayesian framework

Wang

Zhou

Jin

et al. 2018

IPSJ T Comput Vis Appl

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RGB‐D salient object detection via deep fusion of semantics and details

Zhao

Chen

Wang

et al. 2020

Computer Animation & Virtual

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In this paper, we address RGB-D salient object detection task by jointly leveraging semantics and contour details of salient objects. We propose a novel semantics-and-details complementary fusion network to adaptively integrate cross-model and multilevel features. Specifically, we employ two kinds of fusion modules in our model, which are designed for fusing high-level semantic features and integrating contour detail features of the scene components, respectively. The semantics fusion module aggregates high-level interdependent semantic relationships by a nonlinear weighted summation of small and medium receptive fields. Meanwhile, the details module integrates multi-level contour detail features to leverage expressive details of salient objects. We achieve new state-of-the-art salient object detection results on seven RGB-D datasets, that is, STERE, NJU2000, LFSD, NLPR, SSD, DES, and SIP2019 dataset. Experimental results demonstrate that our method outperforms eleven state-of-the-art salient object detection methods. K E Y W O R D S cross-model and multilevel features, feature fusion and deep fusion, RGB-D, salient object detection

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RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques

Ward¹,

Laga

Bennamoun

2019

Advances in Computer Vision and Pattern Recognition

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Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of low cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations, and highlight some important directions for future research.

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Exploiting global priors for RGB-D saliency detection

Cited by 190 publications

References 25 publications

Visual saliency detection for RGB-D images under a Bayesian framework

Visual saliency detection for RGB-D images under a Bayesian framework

RGB‐D salient object detection via deep fusion of semantics and details

RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques

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