The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Frequency-tuned salient region detection
Open accessAbstract
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Olshausen, B., Anderson, C., & Van Essen, D. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-11-04700.1993 · Cited by 908
Cited by
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
Saliency filters: Contrast based filtering for salient region detection
Perazzi, F., Krahenbuhl, P., Pritch, Y., & Hornung, A. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247743 · Cited by 2,110
Structure-Measure: A New Way to Evaluate Foreground Maps
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092
Color balance and fusion for underwater image enhancement
Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2759252 · Cited by 1,357
Visual saliency based on multiscale deep features
Li, G., & Yu, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299184 · Cited by 1,163
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Cheng, M.-M., Zhang, Z., Lin, W.-Y., & Torr, P. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.414 · Cited by 1,050
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
Salient Object Detection via Integrity Learning
Zhuge, M., Fan, D.-P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179526 · Cited by 403
Saliency detection using maximum symmetric surround
Achanta, R., & Susstrunk, S. (2010) · 2010 IEEE International Conference on Image Processing · doi:10.1109/icip.2010.5652636 · Cited by 384
Self-citationBayesian Saliency via Low and Mid Level Cues
Xie, Y., Lu, H., & Yang, M.-H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2216276 · Cited by 367
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping
Wang, W., Shen, J., & Ling, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 348
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Chen, H., & Li, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00322 · Cited by 336
RGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Detection of Co-salient Objects by Looking Deep and Wide
Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0907-4 · Cited by 328
Revisiting Video Saliency: A Large-Scale Benchmark and a New Model
Wang, W., Shen, J., Guo, F., Cheng, M.-M., & Borji, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00514 · Cited by 268
A Single Stream Network for Robust and Real-Time RGB-D Salient Object Detection
Zhao, X., Zhang, L., Pang, Y., Lu, H., & Zhang, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_39 · Cited by 259
A review of the F-measure: its history, properties, criticism, and alternatives
Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Wu, Y.-H., Liu, Y., Xu, J., Bian, J.-W., Gu, Y.-C., & Cheng, M.-M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3134684 · Cited by 176
An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks
Yi, L., Li, G., & Jiang, M. (2017) · steel research international · doi:10.1002/srin.201600068 · Cited by 173
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
Introducing a Bayesian model of selective attention based on active inference
Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83
Zhao, Y., Wu, S., Reynolds, L., & Azenkot, S. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134756 · Cited by 68
Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.001 · Cited by 59
Song, K., & Yan, Y. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/429094 · Cited by 44
Robust Image Hashing With Isomap and Saliency Map for Copy Detection
Liang, X., Tang, Z., Wu, J., Li, Z., & Zhang, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3139217 · Cited by 32
Li, H., Han, T., Wang, J., Lu, Z., Cao, X., Chen, Y., Li, L., Zhou, C., & Chai, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.014 · Cited by 27
Dai, Y., Xue, C., & Zhou, L. (2022) · PLOS ONE · doi:10.1371/journal.pone.0263729 · Cited by 6
A model of co-saliency based audio attention
Zhao, X., Wang, X., & Cheng, D. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09020-3 · Cited by 4
Metadata
- Authors
- Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 1597-1604
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2100470808
- Cited by
- 4,167
- References
- 2
- FWCI
- 83.69
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.