The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Contrast-based image attention analysis by using fuzzy growing
Abstract
Visual attention analysis provides an alternative methodology to semantic image understanding in many applications such as adaptive content delivery and region-based image retrieval. In this paper, we propose a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are threefold: 1) a new saliency map generation method based on local contrast analysis is proposed; 2) by simulating human perception, a fuzzy growing method is used to extract attended areas or objects from the saliency map; and 3) a practicable framework for image attention analysis is presented, which provides three-level attention analysis, i.e., attended view, attended areas and attended points. This framework facilitates visual analysis tools or vision systems to automatically extract attentions from images in a manner like human perception. User study results indicate that the proposed approach is effective and practicable.
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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
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
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
Metadata
- Authors
- Ma, Y.-F., & Zhang, H.-J.
- Venue
- Proceedings of the eleventh ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2003-11-02
- Pages
- 374-381
- OpenAlex
- W2078132377
- Cited by
- 965
- References
- 21
- FWCI
- 13.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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