The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Minimum Barrier Salient Object Detection at 80 FPS
Abstract
We propose a highly efficient, yet powerful, salient object detection method based on the Minimum Barrier Distance (MBD) Transform. The MBD transform is robust to pixel-value fluctuation, and thus can be effectively applied on raw pixels without region abstraction. We present an approximate MBD transform algorithm with 100X speedup over the exact algorithm. An error bound analysis is also provided. Powered by this fast MBD transform algorithm, the proposed salient object detection method runs at 80 FPS, and significantly outperforms previous methods with similar speed on four large benchmark datasets, and achieves comparable or better performance than state-of-the-art methods. Furthermore, a technique based on color whitening is proposed to extend our method to leverage the appearance-based backgroundness cue. This extended version further improves the performance, while still being one order of magnitude faster than all the other leading methods.
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Cites
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Frequency-tuned salient region detection
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Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., & Hu, S.-M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995344 · Cited by 2,976
Saliency Detection via Graph-Based Manifold Ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554
Global Contrast Based Salient Region Detection
Cheng, M.-M., Mitra, N. J., Huang, X., Torr, P. H. S., & Hu, S.-M. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345401 · Cited by 2,463
Saliency filters: Contrast based filtering for salient region detection
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Learning to Detect a Salient Object
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Cited by
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation
Zhang, D., Han, J., Zhang, Y., & Xu, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2900649 · Cited by 118
Surface Defect Saliency of Magnetic Tile
Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K. (2018) · 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560423 · Cited by 117
Densely nested top-down flows for salient object detection
Fang, C., Tian, H., Zhang, D., Zhang, Q., Han, J., & Han, J. (2022) · Science China Information Sciences · doi:10.1007/s11432-021-3384-y · Cited by 98
Metadata
- Authors
- Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., & Mech, R.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1404-1412
- OpenAlex
- W2211996548
- Cited by
- 435
- References
- 35
- FWCI
- 26.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., & Mech, R. (2015). Minimum Barrier Salient Object Detection at 80 FPS. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.165
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