The DOI matches its Crossref record; 222 of the 227 references it cites were found in a registry or an index.
RGB-D salient object detection: A survey
Open accessAbstract
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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Items 201–222 of 222
RGB-D saliency detection via mutual guided manifold ranking
Xue, H., Gu, Y., Li, Y., & Yang, J. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350882 · Cited by 11
RGB-D Saliency Detection by Multi-stream Late Fusion Network
Chen, H., Li, Y., & Su, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4_41 · Cited by 10
Two-Stream Refinement Network for RGB-D Saliency Detection
Liu, D., Hu, Y., Zhang, K., & Chen, Z. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803653 · Cited by 10
Depth combined saliency detection based on region contrast model
Zhang, H., Lei, J., Xiaohong, F., Wu, M., Zhang, P., & Bu, S. (2012) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2012.6295184 · Cited by 9
İmamoğlu, N., Shimoda, W., Zhang, C., Fang, Y., Kanezaki, A., Yanai, K., & Nishida, Y. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1159-7 · Cited by 9
Salient object segmentation based on depth-aware image layering
Du, H., Liu, Z., & Shi, R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6736-4 · Cited by 9
Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection
Wang, Y., Li, Y., Elder, J. H., Wu, R., Lu, H., & Zhang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69532-3_21 · Cited by 8
Visual Saliency Detection for RGB-D Images with Generative Model
Wang, S., Zhou, Z., Qu, H., & Li, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_2 · Cited by 7
HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection
Feng, D. D., Barnes, N. M., & You, S. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227440 · Cited by 7
CNN-based RGB-D Salient Object Detection: Learn, Select and FusePreprint
Chen, H., & Li, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09309 · Cited by 7
RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking
Sun, D., Li, S., Ding, Z., & Luo, B. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-3415-7_57 · Cited by 7
RGB-D saliency detection under Bayesian framework
Wang, S., Zhou, Z. X., Qu, H., & Li, B. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899911 · Cited by 6
Multi-Modal Weights Sharing and Hierarchical Feature Fusion for RGBD Salient Object Detection
Xiao, F., Li, B., Peng, Y., Cao, C., Hu, K., & Gao, X. (2020) · IEEE Access · doi:10.1109/access.2020.2971509 · Cited by 5
Image and Graphics Technologies and Applications
(2019) · Communications in computer and information science · doi:10.1007/978-981-13-9917-6 · Cited by 5
Selective features for RGB-D saliency
Zhu, L., Cao, Z., Fang, Z., Xiao, Y., Wu, J., Deng, H., & Liu, J. (2015) · Chinese Automation Congress · doi:10.1109/cac.2015.7382554 · Cited by 4
Wang, X., Li, S., Chen, C., Hao, A., & Qin, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.04157 · Cited by 3
Saliency detection with relative location measure in light field image
Li, C., Zhan, B., Zhang, S., & Sheng, H. (2017) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc.2017.7984449 · Cited by 2
Co-Saliency Detection for RGBD Images Based on Effective Propagation Mechanism
Jin, Z., Li, J., & Li, D. (2019) · IEEE Access · doi:10.1109/access.2019.2943899 · Cited by 2
Synergistic saliency and depth prediction for RGB-D saliency detectionPreprint
Wang, Y., Li, Y., Elder, J. H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01711 · Cited by 2
Multi-level Cross-modal Interaction Network for RGB-D Salient Object DetectionPreprint
Huang, Z., Chen, H., Zhou, T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14352 · Cited by 2
Self-citationDepth-induced Cellular Automata for Light Field Saliency
Piao, Y., Li, X., & Zhang, M. (2018) · Frontiers in Optics + Laser Science (FiO, LS) · doi:10.1364/fio.2018.fth3e.3 · Cited by 1
Is Depth Really Necessary for Salient Object Detection?Preprint
Zhao, J., Zhao, Y., Li, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.00269 · Cited by 1
Research works printed but not linked: 1
- Lang, C. Y.; Nguyen, T. V.; Katti, H.; Yadati, K.; Kankanhalli, M.; Yan, S. C. Depth matters: influence of depth cues on visual saliency. In: Computer Vision — ECCV 2012. Lecture Notes in Computer Science, Vol. 7573. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 101–115, 2012.
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Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Liu, N., Zhang, N., Wan, K., Shao, L., & Han, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00468 · Cited by 629
Self-citationBayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
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Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., & Shen, D. G. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109555 · Cited by 357
Self-citationHierarchical Alternate Interaction Network for RGB-D Salient Object Detection
Li, G., Liu, Z., Chen, M., Bai, Z., Lin, W., & Ling, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3062689 · Cited by 349
Metadata
- Authors
- Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L.
- Affiliations
- Inception Institute of Artificial Intelligence (AE)
- Nankai University (CN)
- Corresponding author
- Fan, Deng-Ping
- Venue
- Computational Visual Media
- Publisher
- Tsinghua University Press
- Published
- 2021-03
- Volume
- 7
- Issue
- 1
- Pages
- 37-69
- ISSN
- 2096-0433 (ISSN-L)
- 2096-0662
- PMC
- PMC7788385
- OpenAlex
- W3046597754
- Cited by
- 331
- References
- 227
- Citations by year
- 2026: 33 · 2025: 42 · 2024: 50 · 2023: 70 · 2022: 76 · 2021: 50
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computational Visual Media (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61922046, 2018AAA0100400)
- Natural Science Foundation of Tianjin City (17JCJQJC43700)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- salient object detection, RGB-D, depth images, light field, benchmark dataset, visual perception, depth sensors
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