The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Unsupervised extraction of visual attention objects in color images
Abstract
This paper proposes a generic model for unsupervised extraction of viewer's attention objects from color images. Without the full semantic understanding of image content, the model formulates the attention objects as a Markov random field (MRF) by integrating computational visual attention mechanisms with attention object growing techniques. Furthermore, we describe the MRF by a Gibbs random field with an energy function. The minimization of the energy function provides a practical way to obtain attention objects. Experimental results on 880 real images and user subjective evaluations by 16 subjects demonstrate the effectiveness of the proposed approach.
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Citations
Cites
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
Cited by
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
Bayesian 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
Metadata
- Authors
- Han, J., Ngan, K. N., Li, M., & Zhang, H.-J.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2006-01
- Volume
- 16
- Issue
- 1
- Pages
- 141-145
- ISSN
- 1051-8215 (ISSN-L)
- OpenAlex
- W2169041475
- Cited by
- 406
- References
- 1
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
