The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Hierarchical Saliency Detection
Abstract
When dealing with objects with complex structures, saliency detection confronts a critical problem - namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed.
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Citations
Cites
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 1×
“(5), exact inference can be achieved via belief propagation [16].”
BackgroundHierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1×
“Our model finds foundation from studies in psychology [20, 17], which show the selection process in human attention system operates from more than one levels, and the interaction between levels is more complex than a feed-forward scheme.”
Background
Cited by
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
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
CNN: Single-label to Multi-label
Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2014) · arXiv · doi:10.48550/arxiv.1406.5726 · Cited by 784
Zhang, Q., Shen, X., Xu, L., & Jia, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_53 · Cited by 580
Self-citationSalient 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
Metadata
- Authors
- Yan, Q., Xu, L., Shi, J., & Jia, J.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1155-1162
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2002781701
- Cited by
- 1,773
- References
- 3
- FWCI
- 74.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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