The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Saliency Detection via Dense and Sparse Reconstruction
Abstract
In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction errors. The image boundaries are first extracted via super pixels as likely cues for background templates, from which dense and sparse appearance models are constructed. For each image region, we first compute dense and sparse reconstruction errors. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, pixel-level saliency is computed by an integration of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise.
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Cites
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
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
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
Self-citationGlobal 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
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
Self-citationSaliency 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
Learning to Detect a Salient Object
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., & Shum, H.-Y. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.70 · Cited by 1,777
Contrast-based image attention analysis by using fuzzy growing
Ma, Y.-F., & Zhang, H.-J. (2003) · Proceedings of the eleventh ACM international conference on Multimedia · doi:10.1145/957013.957094 · Cited by 965
Cited by
Structure-Measure: A New Way to Evaluate Foreground Maps
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., & Borji, A. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/97 · Cited by 1,607
Salient Object Detection: A Benchmark
Borji, A., Cheng, M.-M., Jiang, H., & Li, J. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2487833 · Cited by 1,364
Deep networks for saliency detection via local estimation and global search
Wang, L., Lu, H., Ruan, X., & Yang, M.-H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298938 · Cited by 676
Self-citationDeeply Supervised Salient Object Detection with Short Connections
Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P. H. S. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815688 · Cited by 676
Metadata
- Authors
- Li, X., Lu, H., Zhang, L., Ruan, X., & Yang, M.-H.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 2976-2983
- OpenAlex
- W2128340050
- Cited by
- 707
- References
- 25
- FWCI
- 36.69
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, X., Lu, H., Zhang, L., Ruan, X., & Yang, M.-H. (2013). Saliency Detection via Dense and Sparse Reconstruction. 2013 IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2013.370
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