Visual saliency detection via rank-sparsity decomposition

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2010, Pages 1089-1092

  Yan, Junchi ^a   Liu, Jian ^a   Li, Yin ^a   Niu, Zhibin ^a   Liu, Yuncai ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Convex optimization; Matrix decomposition; Saliency detection; Sparse coding

Indexed keywords

FEATURE-BASED MODEL; HUMAN VISUAL SYSTEM; IMAGE PATCHES; MATRIX DECOMPOSITION; NATURAL IMAGES; OBJECT DETECTION AND RECOGNITION; OVER-COMPLETE; POTENTIAL APPLICATIONS; SALIENCY DETECTION; SPARSE BASIS; SPARSE CODING; VISUAL SALIENCY;

IMAGING SYSTEMS; OBJECT RECOGNITION; OPTIMIZATION; VISUALIZATION;

CONVEX OPTIMIZATION;

EID: 78651076714     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2010.5652280     Document Type: Conference Paper

References (17)

1. A. Treisman and G. Gelade, "A feature-integration theory of attention," Cognitive Psychology, vol. 12, pp. 97-136, 1980.
2. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," PAMI, vol. 20, no. 11, pp. 1254-1259, 1998.
3. N. Bruce and J. Tsotsos, "Saliency based on information maximization," in Proceedings NIPS, 2005.
4. X. Hou and L. Zhang, "Dynamic visual attention: Searching for coding length increments," in Proceedings NIPS, 2008.
5. D. Gao, V. Mahadevan, and N. Vasconcelos, "On the plausibility of the discriminant center-surround hypothesis for visual saliency," Journal of Vision, vol. 8, pp. 1-18, 2008.
6. E. J. Candés, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis?," preprint, submitted to Journal of the ACM, 2009.
7. J. Wright, Y. Peng, Y. Ma, A. Ganesh, and S. Rao, "Robust principal component analysis: Exact recovery of corrupted low-rank matrices by convex optimization," in Proceedings NIPS, 2009.
8. Z. Lin, M. Chen, L. Wu, and Y. Ma, "The augmented lagrange multiplier method for exact recovery of corrupted lowrank matrices," preprint, 2009.
9. V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky, "Rank-sparsity incoherence for matrix decomposition," preprint, 2009.
10. B. Olshausen and D. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, no. 6583, pp. 607-609, 1996.
11. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," preprint, 2009.
12. J. Liu, S. Ji, and J. Ye, SLEP: Sparse Learning with Efficient Projections.
13. A. Oliva and A. Torralba, "Modeling the shape of the scene: a holistic representation of the spatial envelope," IJCV, vol. 42, pp. 145-175, 2001.
14. B. W. Tatler, R. J. Baddeley, and I.D. Gilchrist, "Visual correlates of fixation selection: Effects of scale and time," Vision Research, vol. 45, pp. 643-659, 2005.
15. D. Walther and C. Koch, "Modeling attention to salient protoobjects," Neural Networks, vol. 19, pp. 1395-1407, 2006.
16. Peter Wonka Ji Liu, Przem Musialski and Jieping Ye, "Tensor completion for estimating missing values in visual data," in Proceedings ICCV, 2009.
17. J. Chen J. Ye L. Sun, J. Liu, "Efficient recovery of jointly sparse vectors," in Proceedings NIPS, 2009.