Visual saliency with statistical priors

International Journal of Computer Vision

Volumn 107, Issue 3, 2014, Pages 239-253

  Li, Jia ^a   Tian, Yonghong ^a   Huang, Tiejun ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

Bayesian framework; Image statistics; Prior knowledge; Visual saliency

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

BAYESIAN FRAMEWORKS; BOTTOM-UP SALIENCIES; IMAGE SALIENCIES; IMAGE STATISTICS; PRIOR KNOWLEDGE; STATE-OF-THE-ART METHODS; VISUAL SALIENCY; VISUAL STIMULUS;

VISUALIZATION;

EID: 84897467150     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-013-0678-0     Document Type: Article

References (43)

1. Achanta, R.; Hemami, S.; Estrada, F.; Susstrunk, S. (2009). Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition.
2. Aziz, M. Z.; Mertsching, B. (2008). Fast and robust generation of feature maps for region-based visual attention. IEEE Transactions on Image Processing, 17(5), 633-644.
3. Borji, A.; Itti, L. (2012). Exploiting local and global patch rarities for saliency detection (pp. 478-485). In: Proc. IEEE Conference on Computer Vision and Pattern Recognition.
4. Bruce, N. D.; Tsotsos, T. K. (2006). Saliency based on information maximization. Advances in Neural Information Processing Systems, 18, 155-162.
5. Cerf, M.; Harel, J.; Einhauser, W.; Koch, C. (2008). Predicting human gaze using low-level saliency combined with face detection (pp. 241-248). In: Advances in Neural Information Processing Systems.
6. Cheng, M. M.; Zhang, G. X.; Mitra, N. J.; Huang, X.; Hu, S. M. (2011). Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition.
7. Chikkerur, S.; Serre, T.; Tan, C.; Poggio, T. (2010). What and where: A bayesian inference theory of attention. Vision Research, 50(22), 2233-2247.
8. Elazary, L.; Itti, L. (2008). Interesting objects are visually salient. Journal of Vision, 8(3), 1-15.
9. Frey, B. J.; Dueck, D. (2007). Clustering by passing messages between data points. Science, 315, 972-976.
10. Frith, C. (2005). The top in top-down attention. In L. Itti, G. Rees, & J. K. Tsotsos (Eds.), Neurobiology of attention (pp. 105-108). San Diego, CA: Elsevier.
11. Goferman, S.; Zelnik-Manor, L.; Tal, A. (2010). Context-aware saliency detection. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition.
12. Han, F.; Zhu, S. C. (2009). Bottom-up/top-down image parsing with attribute grammar. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 59-73.
13. Harel, J.; Koch, C.; Perona, P. (2006). Graph-based visual saliency. Advances in Neural Information Processing Systems, 19, 545-552.
14. Henderson, J. M. (2003). Human gaze control during real-world scene perception. Trends in Cognitive Sciences, 7(11), 498-504.
15. Hou, X.; Zhang, L. (2007). Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition.
16. Hou, X.; Zhang, L. (2008). Dynamic visual attention: Searching for coding length increments. Advances in Neural Information Processing Systems, 21, 681-688.
17. Itti, L.; Koch, C.; Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254-1259.
18. Judd, T.; Ehinger, K.; Durand, F.; Torralba, A. (2009). Learning to predict where humans look. In: IEEE International Conference on Computer Vision.
19. Kienzle, W.; Wichmann F.A.; Scholkopf B.; Franz M.O. (2007). A nonparametric approach to bottom-up visual saliency (pp. 689-696). In: Advances in Neural Information Processing Systems.
20. Li, J.; Tian, Y.; Huang, T.; Gao, W. (2010). Probabilistic multi-task learning for visual saliency estimation in video. International Journal of Computer Vision, 90(2), 150-165.
21. Li, J.; Levine, M. D.; An, X.; Xu, X.; He, H. (2013). Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 996-1010.
22. Liu, H.; Jiang, S.; Huang, Q.; Xu, C.; Gao, W. (2007a). Region-based visual attention analysis with its application in image browsing on small displays (pp. 305-308). In: ACM International Conference on Multimedia.
23. Liu, T.; Sun, J.; Zheng, N. N.; Tang, X.; Shum, H. Y. (2007b). Learning to detect a salient object. In: IEEE Conference on Computer Vision and Pattern Recognition.
24. Lu, Y.; Zhang, W.; Lu, H.; Xue, X. (2011). Salient object detection using concavity context (pp. 233-240). In: IEEE International Conference on Computer Vision.
25. Meur, O. L.; Callet, P. L.; Barba, D.; Thoreau, D. (2006). A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 802-817.
26. Navalpakkam, V.; Itti, L. (2007). Search goal tunes visual features optimally. Neuron, 53, 605-617.
27. Parikh, D.; Zitnick, C.; Chen, T. (2008). Determining patch saliency using low-level context. In: European Conference on Computer Vision.
28. Peters, R. J.; Itti, L. (2007). Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention. In: IEEE Conference on Computer Vision and Pattern Recognition.
29. Riche, N.; Mancas, M.; Gosselin, B.; Dutoit, T. (2012). Rare: a new bottom-up saliency model. In: IEEE International Conference on Image Processing.
30. Sun, X.; Yao, H.; Ji, R. (2012). What are we looking for: Towards statistical modeling of saccadic eye movements and visual saliency (pp. 1552-1559). In: Proc. IEEE Conference on Computer Vision and Pattern Recognition.
31. Tatler, B. W.; Baddeley, R. J.; Gilchrist, I. D. (2005). Visual correlates of fixation selection: Effects of scale and time. Vision Research, 45, 643-659.
32. Torralba, A.; Oliva, A.; Castelhano, M.; Henderson, J. (2006). Contextual guidance of eye movements and attention in real-world scenes: The role of global features on object search. Psychological Review, 113(4), 766-786.
33. Tseng, P. H.; Carmi, R.; Cameron, I. G. M.; Munoz, D. P.; Itti, L. (2009). Quantifying center bias of observers in free viewing of dynamic natural scenes. Journal of Vision, 9(7), 1-16.
34. Vikram, T. N.; Tscherepanow, M.; Wrede, B. (2012). A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recognition, 45, 3114-3124.
35. Walther, D.; Koch, C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19(9), 1395-1407.
36. Wang, W.; Wang, Y.; Huang, Q.; Gao, W. (2010). Measuring visual saliency by site entropy rate. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition.
37. Wolfe, J. M.; Alvarez, G. A.; Horowitz, T. S. (2000). Attention is fast but volition is slow. Nature, 406, 691.
38. Wolfe, J. M. (2005). Guidance of visual search by preattentive information. In L. Itti, G. Rees, & J. Tsotsos (Eds.), Neurobiology of attention (pp. 101-104). San Diego, CA: Academic Press.
39. Wu, T.; Zhu, S. C. (2011). A numerical study of the bottom-up and top-down inference processes in and-or graphs. International Journal of Computer Vision, 93(2), 226-252.
40. Yang, J.; Yang, M. H. (2012). Top-down visual saliency via joint crf and dictionary learning (pp. 2296-2303). In: Proc. IEEE Conference on Computer Vision and Pattern Recognition.
41. Zhang, L.; Tong, M. H.; Marks, T. K.; Shan, H.; Cottrell, G. W. (2008). Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 8(7), 1-20.
42. Zhao, Q.; Koch, C. (2011). Learning a saliency map using fixated locations in natural scenes. Journal of Vision, 11(3), 1-15.
43. Zhao, Q.; Koch, C. (2012). Learning visual saliency by combining feature maps in a nonlinear manner using adaboost. Journal of Vision, 12(6), 1-15.