The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Learning a saliency map using fixated locations in natural scenes
Open accessAbstract
Inspired by the primate visual system, computational saliency models decompose visual input into a set of feature maps across spatial scales in a number of pre-specified channels. The outputs of these feature maps are summed to yield the final saliency map. Here we use a least square technique to learn the weights associated with these maps from subjects freely fixating natural scenes drawn from four recent eye-tracking data sets. Depending on the data set, the weights can be quite different, with the face and orientation channels usually more important than color and intensity channels. Inter-subject differences are negligible. We also model a bias toward fixating at the center of images and consider both time-varying and constant factors that contribute to this bias. To compensate for the inadequacy of the standard method to judge performance (area under the ROC curve), we use two other metrics to comprehensively assess performance. Although our model retains the basic structure of the standard saliency model, it outperforms several state-of-the-art saliency algorithms. Furthermore, the simple structure makes the results applicable to numerous studies in psychophysics and physiology and leads to an extremely easy implementation for real-world applications.
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Cites
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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-citationA saliency-based search mechanism for overt and covert shifts of visual attention
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Cited by
What Do Different Evaluation Metrics Tell Us About Saliency Models?
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., & Durand, F. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815601 · Cited by 802
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
Self-citationDeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
Kruthiventi, S. S. S., Ayush, K., & Babu, R. V. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2710620 · Cited by 478
Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model
Cornia, M., Baraldi, L., Serra, G., & Cucchiara, R. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2851672 · Cited by 474
Metadata
- Authors
- Zhao, Q., & Koch, C.
- Venue
- Journal of Vision
- Published
- 2011-03-10
- Volume
- 11
- Issue
- 3
- Pages
- 9-9
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2151900481
- Cited by
- 256
- References
- 64
- FWCI
- 35.54
- Type
- journal article
- Language
- en
- Open access
- gold
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