Stereoscopic saliency model using contrast and depth-guided-background prior

Liang, Fangfang; Duan, Lijuan; Ma, Wei; Qiao, Yuanhua; Cai, Zhi; Qing, Laiyun

doi:10.1016/j.neucom.2017.10.052

Cited by 63 publications

(32 citation statements)

References 43 publications

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“…We compare our proposed algorithm with 10 state-of-the-art RGB-D saliency detection models, including ACSD [ 26 ], DESM [ 12 ], LHM [ 13 ], GP [ 27 ], DCMC [ 37 ], LBE [ 28 ], SE [ 16 ], CDCP [ 18 ], CDB [ 24 ], and DTM [ 38 ]. For fair comparison, we employ saliency maps provided by the [ 51 ].…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Furthermore, existing RGB-D saliency detection models mainly use depth information in two ways. One is based on depth features [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], which focuses on taking depth information as an explicit supplementary feature of color features. In [ 12 ], Cheng et al calculate the saliency map with additional depth information through color contrast, depth contrast, and spatial bias extended from 2D to 3D, which also proves that depth information is beneficial to visual saliency analysis in complex scenes.…”

Section: Introductionmentioning

confidence: 99%

“…In order to fully explore the potential color and depth cues in the whole saliency processing process, Peng et al [ 16 ] propose an evolution strategy to introduce depth information into super-pixel generation, initial saliency map generation, and saliency propagation. In [ 24 ], Fang et al propose a united stereoscopic saliency model, which combines depth-guided background prior, boundary background, and compactness based on disparity to estimate the initial saliency map. The map is refined by using the spatial dissimilarity features under reduced dimensions and central preference.…”

Section: Introductionmentioning

confidence: 99%

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Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Han

Liu

et al. 2021

Sensors

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The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Han

Liu

et al. 2021

Sensors

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“…To quantify the performance of different models, we conducted a comprehensive evaluation of 24 representative RGB-D based salient object detection models, including nine traditional methods: LHM [51], ACSD [56], DESM [49], GP [50], LBE [57], DCMC [36], SE [37], CDCP [84], CDB [95], and fifteen deep learning-based methods: DF [52], PCF [92], CTMF [58], CPFP [53], TANet [103], AFNet [106], MMCI [55], DMRA [54], D 3 Net [38], SSF [39], A2dele [40], S 2 MA [41], ICNet [42], JL-DCF [43], and UC-Net [44]. We report the mean values of S α and MAE across the five datasets (STERE [139], NLPR [51] , LFSD [140], DES [49], and SIP [38]) for each model in Fig.…”

Section: Overall Evaluationmentioning

confidence: 99%

RGB-D salient object detection: A survey

et al. 2021

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Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

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“…Traditional RGB-D saliency models usually rely on hand-crafted features to distinguish salient objects in given images. Existing widely-used hand-crafted features including contrast [28,38,39], compactness [39,40], center-surround difference [41,42], center or boundary prior [43,44], background enclosure [32], and various fused saliency measures [29]. In Ref.…”

Section: Traditionalmentioning

confidence: 99%

WGI-Net: A weighted group integration network for RGB-D salient object detection

Zhang

Wang

et al. 2021

Comp. Visual Media

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Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

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Stereoscopic saliency model using contrast and depth-guided-background prior

Cited by 63 publications

References 43 publications

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

RGB-D salient object detection: A survey

WGI-Net: A weighted group integration network for RGB-D salient object detection

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