The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Salient object detection via bootstrap learning
Abstract
We propose a bootstrap learning algorithm for salient object detection in which both weak and strong models are exploited. First, a weak saliency map is constructed based on image priors to generate training samples for a strong model. Second, a strong classifier based on samples directly from an input image is learned to detect salient pixels. Results from multiscale saliency maps are integrated to further improve the detection performance. Extensive experiments on six benchmark datasets demonstrate that the proposed bootstrap learning algorithm performs favorably against the state-of-the-art saliency detection methods. Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
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Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
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A model of saliency-based visual attention for rapid scene analysis
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Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
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Frequency-tuned salient region detection
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Saliency Detection: A Spectral Residual Approach
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What energy functions can be minimized via graph cuts?
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Global contrast based salient region detection
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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
Self-citationLearning Uncertain Convolutional Features for Accurate Saliency Detection
Zhang, P., Wang, D., Lu, H., Wang, H., & Yin, B. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.32 · Cited by 481
Self-citationCancer Diagnosis Using Deep Learning: A Bibliographic Review
Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019) · Cancers · doi:10.3390/cancers11091235 · Cited by 422
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Metadata
- Authors
- Tong, N., Lu, H., Ruan, X., & Yang, M.-H.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1884-1892
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1918837316
- Cited by
- 303
- References
- 42
- FWCI
- 24.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tong, N., Lu, H., Ruan, X., & Yang, M.-H. (2015). Salient object detection via bootstrap learning. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298798
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