The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Saliency Detection: A Spectral Residual Approach
Abstract
The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method.
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Cites
A feature-integration theory of attention.
Treisman, A. M., & Gelade, G. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90005-5 · Cited by 12,366
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,319
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
A saliency-based search mechanism for overt and covert shifts of visual attention
Itti, L., & Koch, C. (2000) · Vision Research · doi:10.1016/s0042-6989(99)00163-7 · Cited by 3,156
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
To See or not to See: The Need for Attention to Perceive Changes in Scenes
Rensink, R. A., O'Regan, J. K., & Clark, J. J. (1997) · Psychological Science · doi:10.1111/j.1467-9280.1997.tb00427.x · Cited by 2,338
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211479 · Cited by 2,023
The statistics of natural images
Ruderman, D. (1994) · Network: Computation in Neural Systems · doi:10.1088/0954-898x/5/4/006 · Cited by 941
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
Self-citationVisual 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
Time-Series Anomaly Detection Service at Microsoft
Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., & Zhang, Q. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330680 · Cited by 623
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
Affect-biased attention as emotion regulation
Todd, R. M., Cunningham, W. A., Anderson, A. K., & Thompson, E. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.06.003 · Cited by 336
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
Huang, T., Chen, P., & Li, R. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3511984 · Cited by 61
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
- Hou, X., & Zhang, L.
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2146103513
- Cited by
- 3,422
- References
- 25
- FWCI
- 43.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
