Learning sparse dictionaries for saliency detection

2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012

Volumn , Issue , 2012, Pages

  Guo, Karen ^a   Chen, Hwann Tzong ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE-BASED; SALIENCY DETECTION; SALIENCY MAP; SPARSE CODING; SPARSE DICTIONARIES; SPARSE FEATURES; TRAINING PHASE;

DATA PROCESSING;

IMAGE CODING;

EID: 84874405156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. C. O. Ancuti, C. Ancuti, and P. Bekaert. Enhancing by saliency-guided decolorization. In CVPR, pages 257-264, 2011.
2. S. Avidan and A. Shamir. Seam carving for content-aware image resizing. ACM Trans. Graph., 26(3):10, 2007.
3. A. Borji and L. Itti. Exploiting local and global patch rarities for saliency detection. In CVPR, 2012.
4. N. D. B. Bruce and J. K. Tsotsos. Saliency based on information maximization. In NIPS, 2005.
5. K.-Y. Chang, T.-L. Liu, H.-T. Chen, and S.-H. Lai. Fusing generic objectness and visual saliency for salient object detection. In ICCV, pages 914-921, 2011.
6. K.-Y. Chang, T.-L. Liu, and S.-H. Lai. From co-saliency to cosegmentation: An efficient and fully unsupervised energy minimization model. In CVPR, pages 2129-2136, 2011.
7. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. . least angle regression. Ann. Stat., 32(2), 2004.
8. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. In NIPS, pages 545-552, 2006.
9. L. Itti. Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing, 13(10):1304-1318, 2004.
10. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell., 20(11):1254-1259, 1998.
11. T. Judd, K. A. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In ICCV, pages 2106-2113, 2009.
12. Y. Li, Y. Zhou, L. Xu, X. Yang, and J. Yang. Incremental sparse saliency detection. In ICIP, pages 3093-3096, 2009.
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
14. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11:19-60, 2010.
15. F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In CVPR, 2012.
16. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.
17. J. Wang, L. Quan, J. Sun, X. Tang, and H.-Y. Shum. Picture collage. In CVPR (1), pages 347-354, 2006.
18. M. Wang, J. Konrad, P. Ishwar, K. Jing, and H. A. Rowley. Image saliency: From intrinsic to extrinsic context. In CVPR, pages 417-424, 2011.
19. J. Yang and M.-H. Yang. Top-down visual saliency via joint crf and dictionary learning. In CVPR, 2012.
20. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell. SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision, 8:32-32, 2008.