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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
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
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
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
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
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,424
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
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
Guided Search 2.0 A revised model of visual search
Wolfe, J. M. (1994) · Psychonomic Bulletin & Review · doi:10.3758/bf03200774 · Cited by 3,504
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
IARBUS, A. L. (1967) · Medical Entomology and Zoology · Cited by 3,319
Yarbus, A. L. (1967) · doi:10.1007/978-1-4899-5379-7 · Cited by 3,304
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
Donoho, D. L. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20132 · Cited by 2,490
Foundations of Cyclopean Perception
Richards, W., & Julesz, B. (1973) · The American Journal of Psychology · doi:10.2307/1421958 · Cited by 2,392
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
Elhamifar, E., & Vidal, R. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206547 · Cited by 1,243
SUN: A Bayesian framework for saliency using natural statistics
Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008) · Journal of Vision · doi:10.1167/8.7.32 · Cited by 1,227
Saliency, Scale and Image Description
Kadir, T., & Brady, M. (2001) · International Journal of Computer Vision · doi:10.1023/a:1012460413855 · Cited by 1,195
Saliency Based on Information Maximization
Bruce, N. D. B., & Tsotsos, J. K. (2005) · neural information processing systems · Cited by 1,134
Learning mid-level features for recognition
Boureau, Y.-L., Bach, F., LeCun, Y., & Ponce, J. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539963 · Cited by 1,052
Foundations of Cyclopean Perception
Wilson, H. R., & Julesz, B. (1972) · Chicago Review · doi:10.2307/25294744 · Cited by 973
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
Learning to Detect A Salient Object
Liu, T., Sun, J., Zheng, N.-N., Tang, X., & Shum, H.-Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383047 · Cited by 946
Guo, C., & Zhang, L. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2030969 · Cited by 934
Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention
Itti, L. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.834657 · Cited by 759
Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform
Guo, C., Ma, Q., & Zhang, L. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587715 · Cited by 754
Context-aware saliency detection
Goferman, S., Zelnik-Manor, L., & Tal, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539929 · Cited by 753
Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network
Sha'asua, A., & Ullman, S. (2005) · [1988 Proceedings] Second International Conference on Computer Vision · doi:10.1109/ccv.1988.590008 · Cited by 539
Algorithms for defining visual regions-of-interest: comparison with eye fixations
Privitera, C. M., & Stark, L. W. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.877520 · Cited by 535
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
Siagian, C., & Itti, L. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.40 · Cited by 526
Clustering appearances of objects under varying illumination conditions
Ho, J., Yang, M.-H., Lim, J., Lee, K.-C., & Kriegman, D. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211332 · Cited by 521
Self-citationSegmenting Salient Objects from Images and Videos
Rahtu, E., Kannala, J., Salo, M., & Heikkilä, J. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_27 · Cited by 520
A Metric for Covariance Matrices
Förstner, W., & Moonen, B. (2003) · Geodesy-The Challenge of the 3rd Millennium · doi:10.1007/978-3-662-05296-9_31 · Cited by 460
Local features are not lonely – Laplacian sparse coding for image classification
Gao, S., Tsang, I. W.-H., Chia, L.-T., & Zhao, P. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539943 · Cited by 422
Spatiotemporal Saliency in Dynamic Scenes
Mahadevan, V., & Vasconcelos, N. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.112 · Cited by 409
Unsupervised extraction of visual attention objects in color images
Han, J., Ngan, K. N., Li, M., & Zhang, H.-J. (2006) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2005.859028 · Cited by 406
Class-Specific, Top-Down Segmentation
Borenstein, E., & Ullman, S. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47967-8_8 · Cited by 364
A framework for visual saliency detection with applications to image thumbnailing
Marchesotti, L., Cifarelli, C., & Csurka, G. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459467 · Cited by 322
Boosting color saliency in image feature detection
van de Weijer, J., Gevers, T., & Bagdanov, A. D. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.3 · Cited by 301
Gao, D., Han, S., & Vasconcelos, N. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.27 · Cited by 294
SUN: Top-down saliency using natural statistics
Kanan, C., Tong, M. H., Zhang, L., & Cottrell, G. W. (2009) · Visual Cognition · doi:10.1080/13506280902771138 · Cited by 262
Measuring visual saliency by Site Entropy Rate
Wang, W., Wang, Y., Huang, Q., & Gao, W. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539927 · Cited by 190
Image saliency by isocentric curvedness and color
Valenti, R., Sebe, N., & Gevers, T. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459240 · Cited by 155
Salient object detection by composition
Feng, J., Wei, Y., Tao, L., Zhang, C., & Sun, J. Z. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126348 · Cited by 148
Top-down attention selection is fine grained
Navalpakkam, V., & Itti, L. (2006) · Journal of Vision · doi:10.1167/6.11.4 · Cited by 96
Gao, D., & Vasconcelos, N. M. (2008) · Neural Computation · doi:10.1162/neco.2009.11-06-391 · Cited by 83
Detecting Object Boundaries Using Low-, Mid-, and High-level Information
Zheng, S., Tu, Z., & Yuille, A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383343 · Cited by 51
Automatic foreground extraction for images and videos
Tang, Z., Miao, Z., Wan, Y., & Li, J. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5649226 · Cited by 8
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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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