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A model of saliency-based visual attention for rapid scene analysis
Abstract
A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.
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Cites
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
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CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Self-citationHarel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Self-citationSaliency Detection: A Spectral Residual Approach
Hou, X., & Zhang, L. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383267 · Cited by 3,422
A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
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
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
“M ETHODS for diverting attention to the most important regions of an image and disregarding irrelevant parts are called attention mechanisms; the human visual system uses one [1], [2], [3], [4] to assist in analyzing and understanding complex scenes efficiently and effectively.”
BackgroundSecond-Order Attention Network for Single Image Super-Resolution
Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010
Information content weighting for perceptual image quality assessment
Wang, Z., & Li, Q. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2092435 · Cited by 1,304
A Trainable System for Object Detection
Papageorgiou, C., & Poggio, T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008162616689 · Cited by 1,289
Bayesian surprise attracts human attention
Itti, L., & Baldi, P. (2009) · Vision Research · doi:10.1016/j.visres.2008.09.007 · Cited by 1,224
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
Contrast-based image attention analysis by using fuzzy growing
Ma, Y.-F., & Zhang, H.-J. (2003) · Proceedings of the eleventh ACM international conference on Multimedia · doi:10.1145/957013.957094 · Cited by 965
Robust real-time face detection
Viola, P., & Jones, M. (2005) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937709 · Cited by 898
Five factors that guide attention in visual search
Wolfe, J. M., & Horowitz, T. S. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0058 · Cited by 875
Saliency, attention, and visual search: An information theoretic approach
Bruce, N. D. B., & Tsotsos, J. K. (2009) · Journal of Vision · doi:10.1167/9.3.5 · Cited by 855
Eye guidance in natural vision: reinterpreting salience
Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H. (2011) · Journal of Vision · doi:10.1167/11.5.5 · Cited by 775
Jiang, M., Huang, S., Duan, J., & Zhao, Q. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298710 · Cited by 729
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2509024 · Cited by 665
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
40 years of cognitive architectures: core cognitive abilities and practical applications
Kotseruba, I., & Tsotsos, J. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-018-9646-y · Cited by 539
Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?
Heilbron, M., & Chait, M. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2017.07.061 · Cited by 428
Efficient emotion recognition from speech using deep learning on spectrograms
Satt, A., Rozenberg, S., & Hoory, R. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-200 · Cited by 422
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
Task and context determine where you look
Rothkopf, C. A., Ballard, D. H., & Hayhoe, M. M. (2016) · Journal of Vision · doi:10.1167/7.14.16 · Cited by 366
The Argus® II Retinal Prosthesis System
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Visual Importance Pooling for Image Quality Assessment
Moorthy, A. K., & Bovik, A. C. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2015374 · Cited by 340
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
Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices
Sprague, T. C., & Serences, J. T. (2013) · Nature Neuroscience · doi:10.1038/nn.3574 · Cited by 266
Nessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003037 · Cited by 281
Reward grabs the eye: Oculomotor capture by rewarding stimuli
Theeuwes, J., & Belopolsky, A. V. (2012) · Vision Research · doi:10.1016/j.visres.2012.07.024 · Cited by 237
Active inference on discrete state-spaces: A synthesis
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102447 · Cited by 226
“Winner take-all architectures of decision-making are already commonplace in computational neuroscience (e.g., models of selective attention and recognition [133, 134], hierarchical models of vision [135]).”
BackgroundWhite, B. J., Berg, D. J., Kan, J. Y., Marino, R. A., Itti, L., & Munoz, D. P. (2017) · Nature Communications · doi:10.1038/ncomms14263 · Cited by 191
Self-citationA no-reference perceptual image sharpness metric based on a cumulative probability of blur detection
Narvekar, N. D., & Karam, L. J. (2009) · 2009 International Workshop on Quality of Multimedia Experience · doi:10.1109/qomex.2009.5246972 · Cited by 208
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Ren, M., Pokrovsky, A., Yang, B., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00908 · Cited by 198
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Fu, S., He, H., & Hou, Z.-G. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321570 · Cited by 178
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Nightingale, S. J., Wade, K. A., & Watson, D. G. (2017) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-017-0067-2 · Cited by 135
(2013) · doi:10.1145/2470654.2481374 · Cited by 137
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Meißner, M., & Oll, J. (2019) · Organizational Research Methods · doi:10.1177/1094428117744882 · Cited by 131
Surface Defect Saliency of Magnetic Tile
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Fine, M. S., & Minnery, B. S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5503-08.2009 · Cited by 115
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Krichmar, J. L., & Edelman, G. M. (2005) · Artificial Life · doi:10.1162/1064546053278946 · Cited by 106
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Frey, H. P., Honey, C., & Konig, P. (2008) · Journal of Vision · doi:10.1167/8.14.6 · Cited by 91
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Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83
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
Montijn, J. S., Goltstein, P. M., & Pennartz, C. M. (2015) · eLife · doi:10.7554/elife.10163 · Cited by 46
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Parikh, N., Itti, L., Humayun, M., & Weiland, J. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026017 · Cited by 54
Self-citationOn the Binding Problem in Artificial Neural Networks
Greff, K., van Steenkiste, S., & Schmidhuber, J. (2020) · arXiv · doi:10.48550/arxiv.2012.05208 · Cited by 102
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Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48
Lewis, E. J., Blanco, I., Raila, H., & Joormann, J. (2019) · Emotion · doi:10.1037/emo0000535 · Cited by 43
Qiao, S., Pang, S., Luo, G., Pan, S., Chen, T., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3091579 · Cited by 41
Siontis, K. C., Suárez, A. B., Sehrawat, O., Ackerman, M. J., Attia, Z. I., Friedman, P. A., Noseworthy, P. A., & Maanja, M. (2023) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2023.07.002 · Cited by 38
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Moorthy, A. K., & Bovik, A. C. (2009) · SPIE Proceedings · doi:10.1117/12.810166 · Cited by 37
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Crosby, F., & Hermens, F. (2019) · Quarterly Journal of Experimental Psychology · doi:10.1177/1747021818769203 · Cited by 35
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Zhang, Z., Li, B., Hu, W., & Peng, H. (2020) · arXiv · doi:10.48550/arxiv.2008.02745 · Cited by 32
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Liang, X., Tang, Z., Wu, J., Li, Z., & Zhang, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3139217 · Cited by 32
The argus ii retinal prosthesis system
Bloch, E., & da Cruz, L. (2020) · Prosthesis · doi:10.5772/intechopen.84947 · Cited by 31
Optimization of visual information presentation for visual prosthesis
Guo, F., Yang, Y., & Gao, Y. (2018) · International Journal of Biomedical Imaging · doi:10.1155/2018/3198342 · Cited by 27
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
Park, J., Kwak, I.-Y., & Lim, C. (2021) · Electronics · doi:10.3390/electronics10161996 · Cited by 24
Ma, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24
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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
The dynamic effects of visual complexity and scene cuts on viewer attention
Wals, S., Grewal, D., Wilms, I., Hilken, T., Briassouli, A., & Wetzels, M. (2026) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-025-01137-x · Cited by 2
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Metadata
- Authors
- Itti, L., Koch, C., & Niebur, E.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998
- Volume
- 20
- Issue
- 11
- Pages
- 1254-1259
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2128272608
- Cited by
- 11,271
- References
- 1
- FWCI
- 33.56
- Type
- journal article
- Language
- en
- Open access
- closed
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