The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Robust saliency detection via regularized random walks ranking
Abstract
In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches.
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Cites
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Cited by
Non-local Deep Features for Salient Object Detection
Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., & Jodoin, P.-M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.698 · Cited by 621
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Li, X., Zhao, L., Wei, L., Yang, M.-H., Wu, F., Zhuang, Y., Ling, H., & Wang, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2579306 · Cited by 538
Review of Visual Saliency Detection With Comprehensive Information
Cong, R., Lei, J., Fu, H., Cheng, M.-M., Lin, W., & Huang, Q. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870832 · Cited by 419
Metadata
- Authors
- Li, C., Yuan, Y., Cai, W., Xia, Y., & Feng, D. D.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 2710-2717
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1923594904
- Cited by
- 230
- References
- 2
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, C., Yuan, Y., Cai, W., Xia, Y., & Feng, D. D. (2015). Robust saliency detection via regularized random walks ranking. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298887
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