What do saliency models predict?

Journal of Vision

Volumn 14, Issue 3, 2014, Pages

  Koehler, Kathryn L ^a   Guo, Fei ^a   Zhang, Sheng ^a   Eckstein, Miguel P ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Attention; Eye movements; Real scenes; Saliency; Visual search

Indexed keywords

ADOLESCENT; ARTICLE; ATTENTION; BIOLOGICAL MODEL; EYE FIXATION; EYE MOVEMENT; FEMALE; HUMAN; MALE; PHYSIOLOGY; REAL SCENES; SALIENCY; VISION; VISUAL SEARCH; YOUNG ADULT;

ATTENTION; EYE MOVEMENTS; REAL SCENES; SALIENCY; VISUAL SEARCH;

ADOLESCENT; ATTENTION; EYE MOVEMENTS; FEMALE; FIXATION, OCULAR; HUMANS; MALE; MODELS, NEUROLOGICAL; VISUAL PERCEPTION; YOUNG ADULT;

EID: 84897033720     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/14.3.14     Document Type: Article

References (105)

1. Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009). Frequency-tuned salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009 (pp. 1597- 1604).
2. Avraham, T., and Lindenbaum, M. (2010). Esaliency (extended saliency): Meaningful attention using stochastic image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4), 693-708.
3. Berg, D. J., Boehnke, S. E., Marino, R. A., Munoz, D. P., and Itti, L. (2009). Free viewing of dynamic stimuli by humans and monkeys. Journal of Vision, 9(5):19, 1-15, http://www.journalofvision.org/content/9/5/19, doi:10.1167/9.5.19. [PubMed] [Article]
4. Beutter, B. R., Eckstein, M. P., and Stone, L. S. (2003). Saccadic and perceptual performance in visual search tasks. I. Contrast detection and discrimination. Journal of the Optical Society of America, A: Optics, Image Science, and Vision, 20(7), 1341-1355.
5. Bian, P., and Zhang, L. (2009). Biological plausibility of spectral domain approach for spatiotemporal visual saliency. In Advances in Neuro-Information Processing (pp. 251-258). Berlin: Springer.
6. Birmingham, E., Bischof, W. F., and Kingstone, A. (2009). Saliency does not account for fixations to eyes within social scenes. Vision Research, 49(24), 2992-3000.
7. Bonev, B., Chuang, L. L., and Escolano, F. (2013). How do image complexity, task demands and looking biases influence human gaze behavior? Pattern Recognition Letters, 34(7), 723-730, doi:10.1016/j. patrec.2012.05.007.
8. Borji, A., and Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 185-207. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6180177.
9. Borji, A., Sihite, D. N., and Itti, L. (2012). Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Transactions on Image Processing, 22(1), 55-69. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6253254.
10. Borji, A., Sihite, D. N., and Itti, L. (2013). What stands out in a scene? A study of human explicit saliency judgment. Vision Research, 91, 62-77, doi:10.1016/ j.visres.2013.07.016.
11. Bruce, N. (2006). Retrieved November 2011. from http://www-sop.inria.fr/members/Neil.Bruce/#SOURCECODE
12. Bruce, N., and Tsotsos, J. (2006). Saliency based on information maximization. Advances in Neural Information Processing Systems, 18, 155.
13. Bruce, N. D., and Tsotsos, J. K. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9(3):5, 1-24, http://www.journalofvision.org/contents/9/3/5, doi: 10.1167/9.3.5. [PubMed] [Article]
14. Buswell, G. T. (1935). How people look at pictures. Chicago: University of Chicago Press. Retrieved from http://psych.wfu.edu/art_schirillo/articles/Buswell,%201935.pdf
15. Cerf, M., Frady, E. P., and Koch, C. (2009). Faces and text attract gaze independent of the task: Experimental data and computer model. Journal of Vision, 9(12):10, 1-15, http://www.journalofvision.org/contents/9/12/10, doi:10.1167/9.12.10. [PubMed] [Article]
16. Cerf, M., Harel, J., Einhäuser, W., and Koch, C. (2008). Predicting human gaze using low-level saliency combined with face detection. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Advances in Neural Information Processing Systems, 20. Cambridge, MA: MIT Press.
17. Chen, X., and Zelinsky, G. J. (2006). Real-world visual search is dominated by top-down guidance. Vision Research, 46(24), 4118-4133, doi:10.1016/j.visres. 2006.08.008.
18. DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44(3), 837- 845.
19. Eckstein, M. P. (2011). Visual search: A retrospective. Journal of Vision, 11(5):14, 1-36, http://www.journalofvision.org/content/11/5/14, doi:10.1167/ 11.5.14. [PubMed] [Article]
20. Eckstein, M. P., Beutter, B. R., Pham, B. T., Shimozaki, S. S., and Stone, L. S. (2007). Similar neural representations of the target for saccades and perception during search. Journal of Neuroscience, 27(6), 1266-1270, doi:10.1523/JNEUROSCI. 3975-06.2007.
21. Eckstein, M. P., Drescher, B. A., and Shimozaki, S. S. (2006). Attentional cues in real scenes, saccadic targeting, and Bayesian priors. Psychological Science, 17(11), 973-980, doi:10.1111/j.1467-9280. 2006.01815.x.
22. Einhäuser, W., Rutishauser, U., and Koch, C. (2008). Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli. Journal of Vision, 8(2):2, 1-19, http://www.journalofvision.org/content/8/2/2, doi:10.1167/8.2.2.
23. Einhäuser, W., Spain, M., and Perona, P. (2008). Objects predict fixations better than early saliency. Journal of Vision, 8(14):18, 1-26, http://www.journalofvision.org/content/8/14/18, doi:10.1167/8.14.18. [PubMed] [Article]
24. Erdem, E., and Erdem, A. (2013). Visual saliency estimation by nonlinearly integrating features using region covariances. Journal of Vision, 13(4):11, 1- 20, http://www.journalofvision.org/content/13/4/11, doi:10.1167/13.4.11. [PubMed] [Article]
25. Fang, Y., Chen, Z., Lin, W., and Lin, C.-W. (2012). Saliency detection in the compressed domain for adaptive image retargeting. IEEE Transactions on Image Processing, 21(9), 3888-3901.
26. Findlay, J. M. (1997). Saccade target selection during visual search. Vision Research, 37(5), 617-631.
27. Foulsham, T., and Underwood, G. (2008). What can saliency models predict about eye movements? Spatial and sequential aspects of fixations during encoding and recognition. Journal of Vision, 8(2):6, 1-17, http://www.journalofvision.org/content/8/2/6, doi:10.1167/8.2.6. [PubMed] [Article]
28. Gao, D., Mahadevan, V., and Vasconcelos, N. (2008). On the plausibility of the discriminant centersurround hypothesis for visual saliency. Journal of Vision, 8(7):13, 1-18, http://www.journalofvision.org/content/8/7/13, doi:10.1167/8.7.13. [PubMed] [Article]
29. Garcia-Diaz, A., Fdez-Vidal, X. R., Pardo, X. M., and Dosil, R. (2012). Saliency from hierarchical adaptation through decorrelation and variance normalization. Image and Vision Computing, 30(1), 51-64.
30. Garcia-Diaz, A., Leborán, V., Fdez-Vidal, X. R., and Pardo, X. M. (2012). On the relationship between optical variability, visual saliency, and eye fixations: A computational approach. Journal of Vision, 12(6):17, 1-22, http://www.journalofvision.org/content/12/6/17, doi:10.1167/12.6.17. [PubMed] [Article]
31. Goferman, S., Zelnik-Manor, L., and Tal, A. (2010). Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10), 1915-1926.
32. Guo, C., and Zhang, L. (2010). A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Transactions on Image Processing, 19(1), 185-198.
33. Harding, G., and Bloj, M. (2010). Real and predicted influence of image manipulations on eye movements during scene recognition. Journal of Vision, 10(2):8, 1-17, http://www.journalofvision.org/content/10/2/8, doi:10.1167/10.2.8. [PubMed] [Article]
34. Harel, J., Koch, C., and Perona, P. (2007). Graph-based visual saliency. Advances in Neural Information Processing Systems, 19, 545.
35. Hou, X., Harel, J., and Koch, C. (2012). Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1), 194-201.
36. Hou, X., and Zhang, L. (2007). Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition, 17-22 June, 2007 (pp. 1-8).
37. Hou, X., and Zhang, L. (2008). Dynamic visual attention: Searching for coding length increments. In Advances in Neural Information Processing Systems (pp. 681-688). Cambridge, MA: MIT Press.
38. Itti, L. (2004). Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing, 13(10), 1304-1318.
39. Itti, L., and Baldi, P. (2005). A principled approach to detecting surprising events in video. In Y. Weiss, B. Scholkopf, and J. Platt (Eds.), IEEE Conference on Computer Vision and Pattern Recognition, (pp. 631- 637). Cambridge, MA: MIT Press.
40. Itti, L., and Baldi, P. (2006). Bayesian surprise attracts human attention. Advances in Neural Information Processing Systems, 18, 547.
41. Itti, L., and Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40(10-12), 1489-1506, doi:10.1016/S0042-6989(99)00163-7.
42. Itti, L., Koch, C., and 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.
43. Jacobson, N., and Nguyen, T. (2011). Video processing with scale-aware saliency: Application to frame rate up-conversion. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference, 22-27 May, 2011, doi:10.1109/ICASSP. 2011.5946653.
44. Judd, T., Ehinger, K., Durand, F., and Torralba, A. (2009). Learning to predict where humans look. In IEEE 12th International Conference on Computer Vision, September 29, 2009 (pp. 2106-2113).
45. Jung, C., and Kim, C. (2012). A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Transactions on Image Processing, 21(3), 1272-1283.
46. Kim, C., and Milanfar, P. (2013). Visual saliency in noisy images. Journal of Vision, 13(4):5, 1-14, http://www.journalofvision.org/content/13/4/5, doi:10. 1167/13.4.5. [PubMed] [Article]
47. Koch, C., and Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4), 219-27.
48. Koehler, K., Guo, F., Zhang, S., and Eckstein, M. (2011). Assessing models of visual saliency against explicit saliency judgments from one hundred humans viewing eight hundred real scenes. Journal of Vision, 11(11):165, http://www.journalofvision.org/content/11/11/165, doi:10.1167/11.11.165. [Abstract]
49. Kollmorgen, S., Nortmann, N., Schröder, S., and König, P. (2010). Influence of low-level stimulus features, task dependent factors, and spatial biases on overt visual attention. PLoS Computational Biology, 6(5), e1000791, doi:10.1371/journal.pcbi.1000791.
50. Kootstra, G., and Schomaker, L. R. (2009). Prediction of human eye fixations using symmetry. In Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 56-61). Retrieved from http://csjarchive.cogsci.rpi.edu/proceedings/2009/index.html.
51. Kowler, E., and Blaser, E. (1995). The accuracy and precision of saccades to small and large targets. Vision Research, 35(12), 1741-1754.
52. Lang, C., Liu, G., Yu, J., and Yan, S. (2012). Saliency detection by multitask sparsity pursuit. IEEE Transactions on Image Processing, 21(3), 1327- 1338.
53. Le Meur, O., Le Callet, P., and Barba, D. (2007). Predicting visual fixations on video based on lowlevel visual features. Vision Research, 47(19), 2483- 2498.
54. Le Meur, O., Le Callet, P., Barba, D., and 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.
55. Le Meur, O., Thoreau, D., Le Callet, P., and Barba, D. (2005). A spatio-temporal model of the selective human visual attention. In IEEE International Conference on Image Processing, 11-14 September, 2005 (Vol. 3, pp. III-1188).
56. Lee, W.-F., Huang, T.-H., Yeh, S.-L., and Chen, H. H. (2011). Learning-based prediction of visual attention for video signals. IEEE Transactions on Image Processing, 20(11), 3028-3038.
57. Li, J., Levine, M. D., An, X., Xu, X., and 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. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6243147.
58. Li, J., Tian, Y., Huang, T., and Gao, W. (2009). A dataset and evaluation methodology for visual saliency in video. In IEEE International Conference on Multimedia and Expo, 28 June-3 July, 2009 (pp. 442-445).
59. Li, J., Tian, Y., Huang, T., and Gao, W. (2010). Probabilistic multi-task learning for visual saliency estimation in video. International Journal of Computer Vision, 90(2), 150-165.
60. Li, J., Xu, D., and Gao, W. (2011). Removing label ambiguity in learning-based visual saliency estimation. IEEE Transactions on Image Processing, 21(4), 1513-1525.
61. Li, Y., Zhou, Y., Yan, J., Niu, Z., and Yang, J. (2010). Visual saliency based on conditional entropy. In Computer Vision-ACCV, 2009 (pp. 246-257). Berlin Hiedelberg: Springer.
62. Lin, Y., Tang, Y., Fang, B., Shang, Z., Huang, Y., and Wang, S. (2013). A visual-attention model using earth mover's distance based saliency measurement and nonlinear feature combination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2), 314-328. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6205759.
63. Liu, T., Sun, J., Zheng, N. N., Tang, X., and Shum, H. Y. (2007). Learning to detect a salient object. In IEEE Conference on Computer Vision and Pattern Recognition, 33(2), 353-367.
64. Malcolm, G. L., and Henderson, J. M. (2009). The effects of target template specificity on visual search in real-world scenes: evidence from eye movements. Journal of Vision, 9(11):8, 1-13, http://www.journalofvision.org/content/9/11/8, doi:10.1167/9. 11.8. [PubMed] [Article]
65. Malcolm, G. L., and Henderson, J. M. (2010). Combining top-down processes to guide eye movements during real-world scene search. Journal of Vision, 10(2):4, 1-11, http://www.journalofvision.org/content/10/2/4, doi:10.1167/10.2.4. [PubMed] [Article]
66. Marat, S., Phuoc, T. H., Granjon, L., Guyader, N., Pellerin, D., and Guérin-Dugué, A. (2009). Modelling spatio-temporal saliency to predict gaze direction for short videos. International Journal of Computer Vision, 82(3), 231-243.
67. Masciocchi, C. M., Mihalas, S., Parkhurst, D., and Niebur, E. (2009). Everyone knows what is interesting: Salient locations which should be fixated. Journal of Vision, 9(11):25, 1-22, http://www.journalofvision.org/content/9/11/25, doi:10. 1167/9/11/25. [PubMed] [Article]
68. Murray, N., Vanrell, M., Otazu, X., and Parraga, C. A. (2011). Saliency estimation using a non-parametric low-level vision model. In IEEE Conference on Computer Vision and Pattern Recognition, 20-25 June, 2011 (pp. 433-440).
69. Najemnik, J., and Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387-391.
70. Navalpakkam, V., and Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45(2), 205-231.
71. Nuthmann, A., and Henderson, J. M. (2010). Objectbased attentional selection in scene viewing. Journal of Vision, 10(8):20, 1-19, http://www.journalofvision.org/content/10/8/20, doi:10.1167/10.8.20. [PubMed] [Article]
72. Oliva, A., Torralba, A., Castelhano, M. S., and Henderson, J. M. (2003). Top-down control of visual attention in object detection. In Proceedings of the International Conference on Image Processing, 1, 253-256.
73. Pajak, M., and Nuthmann, A. (2013). Object-based saccadic selection during scene perception: Evidence from viewing position effects. Journal of Vision, 13(5):2, 1-21, http://www.journalofvision.org/content/13/5/2, doi:10.1167/13.5.2. [PubMed] [Article]
74. Parkhurst, D., Law, K., and Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107-123, doi:10. 1016/S0042-6989(01)00250-4.
75. Peters, R. J., Iyer, A., Itti, L., and Koch, C. (2005). Components of bottom-up gaze allocation in natural images. Vision Research, 45(18), 2397-2416.
76. Ramanathan, S., Katti, H., Sebe, N., Kankanhalli, M., and Chua, T. S. (2010). An eye fixation database for saliency detection in images. In K. Daniilidis, P. Maragos, and N. Paragois (Eds.), Computer Vision- ECCV (pp. 30-43). Berlin Heidelberg: Springer.
77. Rao, R. P. N., Hayhoe, M. M., Zelinsky, G. J., and Ballard, D. H. (1996). Modeling saccadic targeting in visual search. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8 (NIPS*95). Cambridge, MA: MIT Press.
78. Rao, R. P. N., Zelinsky, G. J., Hayhoe, M. M., and Ballard, D. H. (2002). Eye movements in iconic visual search. Vision Research, 42(11), 1447-1463.
79. Renninger, L. W., Coughlan, J., Verghese, P., and Malik, J. (2005). An information maximization model of eye movements. Advances in Neural Information Processing Systems, 17, 1121-1128.
80. Rothkopf, C. A., Ballard, D. H., and Hayhoe, M. M. (2007). Task and context determine where you look. Journal of Vision, 7(14):16, 1-20, http://www.journalofvision.org/content/7/14/16, doi:10.1167/7. 14.16. [PubMed] [Article]
81. Russell, B. C., Torralba, A., Murphy, K. P., and Freeman, W. T. (2008). LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision, 77(1-3), 157-173.
82. Seo, H. J., and Milanfar, P. (2009). Static and space-time visual saliency detection by self-resemblance. Journal of Vision, 9(12):15, 1-27, http://www.journalofvision.org/content/9/12/15, doi:10.1167/9.12.15. [PubMed] [Article]
83. Smith, T. J., and Mital, P. K. (2011). Watching the world go by: Attentional prioritization of social motion during dynamic scene viewing. Journal of Vision, 11(11):478, http://www.journalofvision.org/content/11/11/478, doi:10.1167/11.11.478. [Abstract]
84. Tatler, B. W. (2007). The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision, 7(14):4, 1-17, http://www.journalofvision.org/content/7/14/4, doi:10.1167/7.14.4. [PubMed] [Article]
85. Tatler, B. W., Baddeley, R. J., and Gilchrist, I. D. (2005). Visual correlates of fixation selection: Effects of scale and time. Vision Research, 45(5), 643-659.
86. Tatler, B. W., Hayhoe, M. M., Land, M. F., and Ballard, D. H. (2011). Eye guidance in natural vision: Reinterpreting salience. Journal of Vision, 11(5):5, 1-23, http://www.journalofvision.org/content/11/5/5, doi:10.1167/11.5.5. [PubMed] [Article]
87. Tavakoli, H. R., Rahtu, E., and Heikkilä, J. (2011). Fast and efficient saliency detection using sparse sampling and kernel density estimation. In A. Heyden and F. Kahl (Eds.), Image Analysis (pp. 666- 675). Berlin Heidelberg: Springer.
88. Tavassoli, A., van der Linde, I., Bovik, A. C., and Cormack, L. K. (2009). Eye movements selective for spatial frequency and orientation during active visual search. Vision Research, 49(2), 173-181, doi: 10.1016/j.visres.2008.10.005.
89. Toet, A. (2011). Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), 2131-2146.
90. Torralba, A., Oliva, A., Castelhano, M. S., and Henderson, J. M. (2006). Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search. Psychological Review, 113(4), 766.
91. Underwood, G., and Foulsham, T. (2006). Visual saliency and semantic incongruency influence eye movements when inspecting pictures. Quarterly Journal of Experimental Psychology, 59(11), 1931- 1949, doi:10.1080/17470210500416342.
92. Vazquez, E., Gevers, T., Lucassen, M., van de Weijer, J., and Baldrich, R. (2010). Saliency of color image derivatives: A comparison between computational models and human perception. JOSA A, 27(3), 613-621.
93. Vig, E., Dorr, M., Martinetz, T., and Barth, E. (2012). Intrinsic dimensionality predicts the saliency of natural dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(6), 1080-1091.
94. Walther, D. (2006). The saliency toolbox. Retrieved November 2011 from http://www.saliencytoolbox.net
95. Walther, D., and Koch, C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19(9), 1395-1407.
96. Wang, H.-C., and Pomplun, M. (2012). The attraction of visual attention to texts in real-world scenes. Journal of Vision, 12(6):26, 1-17, http://www.journalofvision.org/content/12/6/26, doi:10.1167/ 12.6.26. [PubMed] [Article]
97. Xie, Y., Lu, H., and Yang, M. (2012). Bayesian saliency via low and mid level cues. IEEE Transactions on Image Processing, 22(5), 1689-1698. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6291786.
98. Yan, J., Liu, J., Li, Y., Niu, Z., and Liu, Y. (2010). Visual saliency detection via rank-sparsity decomposition. In IEEE International Conference on Image Processing, 26-29 September, 2010 (pp. 1089-1092).
99. Yun, K., Peng, Y., Samaras, D., Zelinsky, G. J., and Berg, T. (2013). Studying relationships between human gaze, description, and computer vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23-28 June, 2013 (pp. 739- 746).
100. Zelinsky, G., Zhang, W., Yu, B., Chen, X., and Samaras, D. (2005). The role of top-down and bottom-up processes in guiding eye movements during visual search. In Y. Weiss, B. Scholkopf, and J. Platt (Eds.), Advances in Neural Information Processing Systems (pp. 1569-1576) Cambridge, MA: MIT Press.
101. Zhang, L. (2008). Retrieved November, 2011 from http://cseweb.ucsd.edu/~l6zhang/
102. Zhang, L., Tong, M. H., Marks, T. K., Shan, H., and Cottrell, G. W. (2008). SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision, 8(7):32, 1-20, http://www.journalofvision.org/content/8/7/32, doi:10.1167/8.7.32. [PubMed] [Article]
103. Zhang, S., and Eckstein, M. P. (2010). Evolution and optimality of similar neural mechanisms for perception and action during search. PLoS Computational Biology, 6(9), doi:10.1371/journal.pcbi. 1000930.
104. Zhao, Q., and Koch, C. (2011). Learning a saliency map using fixated locations in natural scenes. Journal of Vision, 11(3):9, 1-15, http://www.journalofvision.org/content/11/3/9, doi:10.1167/11.3.9. [PubMed] [Article]
105. Zhao, Q., and Koch, C. (2012). Learning visual saliency by combining feature maps in a nonlinear manner using AdaBoost. Journal of Vision, 12(6):22, 1-15, http://www.journalofvision.org/content/12/6/22, doi:10.1167/12.6.22. [PubMed] [Article]