The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Bayesian Saliency via Low and Mid Level Cues
Abstract
Visual saliency detection is a challenging problem in computer vision, but one of great importance and numerous applications. In this paper, we propose a novel model for bottom-up saliency within the Bayesian framework by exploiting low and mid level cues. In contrast to most existing methods that operate directly on low level cues, we propose an algorithm in which a coarse saliency region is first obtained via a convex hull of interest points. We also analyze the saliency information with mid level visual cues via superpixels. We present a Laplacian sparse subspace clustering method to group superpixels with local features, and analyze the results with respect to the coarse saliency region to compute the prior saliency map. We use the low level visual cues based on the convex hull to compute the observation likelihood, thereby facilitating inference of Bayesian saliency at each pixel. Extensive experiments on a large data set show that our Bayesian saliency model performs favorably against the state-of-the-art algorithms.
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Cites
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Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,176
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An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
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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
Self-citationStructure-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
Detection of Co-salient Objects by Looking Deep and Wide
Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0907-4 · Cited by 328
Metadata
- Authors
- Xie, Y., Lu, H., & Yang, M.-H.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2013-05
- Volume
- 22
- Issue
- 5
- Pages
- 1689-1698
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2159772167
- Cited by
- 367
- References
- 53
- FWCI
- 33.44
- Type
- journal article
- Language
- en
- Open access
- closed
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