The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Saliency filters: Contrast based filtering for salient region detection
Abstract
Saliency estimation has become a valuable tool in image processing. Yet, existing approaches exhibit considerable variation in methodology, and it is often difficult to attribute improvements in result quality to specific algorithm properties. In this paper we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation. Our algorithm consists of four basic steps. First, our method decomposes a given image into compact, perceptually homogeneous elements that abstract unnecessary detail. Based on this abstraction we compute two measures of contrast that rate the uniqueness and the spatial distribution of these elements. From the element contrast we then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We show that the complete contrast and saliency estimation can be formulated in a unified way using high-dimensional Gaussian filters. This contributes to the conceptual simplicity of our method and lends itself to a highly efficient implementation with linear complexity. In a detailed experimental evaluation we analyze the contribution of each individual feature and show that our method outperforms all state-of-the-art approaches.
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Cites
A model of saliency-based visual attention for rapid scene analysis
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An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
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
Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry
Koch, C., & Ullman, S. (1987) · Matters of Intelligence · doi:10.1007/978-94-009-3833-5_5 · Cited by 3,722
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
Global contrast based salient region detection
Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., & Hu, S.-M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995344 · Cited by 2,976
Learning to Detect a Salient Object
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Modeling the role of salience in the allocation of overt visual attention
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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
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
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Cheng, M.-M., Zhang, Z., Lin, W.-Y., & Torr, P. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.414 · Cited by 1,050
RGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
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
- Perazzi, F., Krahenbuhl, P., Pritch, Y., & Hornung, A.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 733-740
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1982075130
- Cited by
- 2,110
- References
- 29
- FWCI
- 79.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Perazzi, F., Krahenbuhl, P., Pritch, Y., & Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247743
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