The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Saliency detection using maximum symmetric surround
Open accessAbstract
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Recently, full-resolution salient maps that retain well-defined boundaries have attracted attention. In these maps, boundaries are preserved by retaining substantially more frequency content from the original image than older techniques. However, if the salient regions comprise more than half the pixels of the image, or if the background is complex, the background gets highlighted instead of the salient object. In this paper, we introduce a method for salient region detection that retains the advantages of such saliency maps while overcoming their shortcomings. Our method exploits features of color and luminance, is simple to implement and is computationally efficient. We compare our algorithm to six state-of-the-art salient region detection methods using publicly available ground truth. Our method outperforms the six algorithms by achieving both higher precision and better recall. We also show application of our saliency maps in an automatic salient object segmentation scheme using graph-cuts.
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Cites
A model of saliency-based visual attention for rapid scene analysis
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Frequency-tuned salient region detection
Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206596 · Cited by 4,167
Self-citationShifts in Selective Visual Attention: Towards the Underlying Neural Circuitry
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Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Boykov, Y. Y., & Jolly, M.-P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937505 · Cited by 3,674
Harel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Saliency 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
Modeling visual attention via selective tuning
Tsotsos, J. K., Culhane, S. M., Kei Wai, W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00025-9 · Cited by 1,143
Contrast-based image attention analysis by using fuzzy growing
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Salient Region Detection and Segmentation
Achanta, R., Estrada, F., Wils, P., & Süsstrunk, S. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79547-6_7 · Cited by 844
Self-citationAn Affine Invariant Salient Region Detector
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Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K. (2018) · 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560423 · Cited by 117
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Metadata
- Authors
- Achanta, R., & Susstrunk, S.
- Venue
- 2010 IEEE International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2010-09
- Pages
- 2653-2656
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2068194118
- Cited by
- 384
- References
- 16
- FWCI
- 20.34
- Type
- conference paper
- Language
- en
- Open access
- green
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