Learning a saliency map using fixated locations in natural scenes

Journal of Vision

Volumn 11, Issue 3, 2011, Pages 1-5

  Zhao, Qi ^a   Koch, Christof ^a,b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b Korea University   (South Korea)

Author keywords

Center bias; Computational saliency model; Feature combination; Inter subject variability; Metric

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; EYE FIXATION; EYE MOVEMENT; HUMAN; OCULOGRAPHY; PATTERN RECOGNITION; PHYSIOLOGY; RECEPTIVE FIELD; REGRESSION ANALYSIS; VISION;

ALGORITHMS; EYE MOVEMENT MEASUREMENTS; EYE MOVEMENTS; FIELD DEPENDENCE-INDEPENDENCE; FIXATION, OCULAR; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, BIOLOGICAL; PATTERN RECOGNITION, VISUAL; VISUAL PERCEPTION;

EID: 79957836414     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/11.3.1     Document Type: Article

References (67)

1. Bahill, A., Adler, D., & Stark, L. (1975). Most naturally occurring human saccades have magnitudes of 15 degrees or less. Investigative Ophthalmology & Visual Science, 14, 468-469.
2. Brandt, S. A., & Stark, L. W. (1997). Spontaneous eye movements during visual imagery reflect the content of the visual scene. Journal of Cognitive Neuroscience, 9, 27-38.
3. Bruce, N., & Tsotsos, J. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9(3):5, 1-24, http://www.journalofvision.org/content/9/3/5, doi:10.1167/9.3.5. [PubMed] [Article]
4. Burgess, A., & Ghandeharian, H. (1984). Visual signal detection: I. Ability to use phase information. Journal of the Optical Society of America A: Optics and Image Science, 1, 900-905.
5. Cerf, M., Frady, E., & 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/content/9/12/10, doi:10.1167/9.12.10.
6. Chikkerur, S. S., Serre, T., Tan, C., & Poggio, T. (2010). What and where: A Bayesian inference theory of attention. Vision Research, 50, 2233-2247.
7. Choi, Y. S., Mosley, A. D., & Stark, L. W. (1995). String editing analysis of human visual search. Optometry and Vision Science, 72, 439-451.
8. Donk, M., & van Zoest, W. (2008). Effects of salience are short-lived. Psychological Science, 19, 733-739.
9. Ehinger, K., Hidalgo-Sotelo, B., Torralba, A. & Oliva, A. (2009). Modelling search for people in 900 scenes: A combined source model of eye guidance. Visual Cognition, 17, 945-978.
10. Einhäuser, W., Rutishauser, U., Frady, E., Nadler, S., Käonig, P., & Koch, C. (2006). The relation of phase noise and luminance contrast to overt attention in complex visual stimuli. Journal of Vision, 6(11):1, 1148-1158, http://www.journalofvision.org/content/6/11/1, doi:10.1167/6.11.1.
11. Einhäuser,W., Spain, M., & 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]
12. Foulsham, T., & 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, 601-617, http://www.journalofvision.org/content/8/2/6, doi:10.1167/8.2.6.
13. Fuller, J. (1996). Eye position and target amplitude effects on human visual saccadic latencies. Experimental Brain Research, 109, 457-466.
14. Gajewski, D., Pearson, A., Mack, M., Bartlett, F., & Henderson, J. (2005). Human gaze control in real world search. In L. Paletta, J. Tsotsos, E. Rome, & G. Humphreys (Eds.), Attention and performance in computational vision (vol. 3368, pp. 83-99). New York: Springer-Verlag.
15. Gao, D., Mahadevan, V., & Vasconcelos, N. (2007). The discriminant center-surround hypothesis for bottomup saliency. In Advances in neural information processing systems (pp. 497-504). MIT Press.
16. Gill, P., Murray, W., & Wright, M. (1981). Practical optimization. London: Academic Press.
17. Green, D., & Swets, J. (1966). Signal detection theory and psychophysics. New York: John Wiley.
18. Hacisalihzade, S. S., Allen, J. S., & Stark, L. (1992). Visual perception and sequences of eye movement fixations: A stochastic modelling approach. IEEE Transactions on Systems, Man and Cybernetics, 22, 474-481.
19. Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In Advances in neural information processing systems (pp. 545-552). MIT Press.
20. Henderson, J. M., Brockmole, J. R., Castelhano, M. S., & Mack, M. L. (2007). Visual saliency does not account for eye movements during visual search in real-world scenes. In R. van Gompel, M. Fischer, W. Murray, & R. W. Hill (Eds.), Eye movements: A window on mind and brain (pp. 537-562). Amsterdam: Elsevier.
21. Hou, X., & Zhang, L. (2008). Dynamic visual attention: Searching for coding length increments. In Advances in neural information processing systems (pp. 681-688). MIT Press.
22. Itti, L. (2005). Models of bottom-up attention and saliency. In L. Itti, G. Rees, & J. K. Tsotsos (Eds.), Neurobiology of attention (pp. 576-582). San Diego, CA: Elsevier.
23. Itti, L., & Baldi, P. (2006). Bayesian surprise attracts human attention. In Advances in neural information processing systems (pp. 547-554). MIT Press.
24. Itti, L., & Koch, C. (1999). Comparison of feature combination strategies for saliency-based visual attention systems. In Proceedings of SPIE human vision and electronic imaging (vol. 3644, pp. 473-482). SPIE-International Society for Optical Engine.
25. Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 1489-1506.
26. Itti, L., Koch, C., & Niebur, E. (1998). A model for saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1254-1259.
27. Jost, T., Ouerhani, N., von Wartburg, R., Mäuri, R., & Häugli, H. (2005). Assessing the contribution of color in visual attention. Computer Vision and Image Understanding, 100, 107-123.
28. Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In IEEE International Conference on Computer Vision (pp. 2106-2113). IEEE Computer Society.
29. Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4, 219-227.
30. Koene, A., & Zhaoping, L. (2007). Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom-up saliency map in V1. Journal of Vision, 7(7):6, 1-14, http://www.journalofvision.org/content/7/7/6, doi:10.1167/7.7.6. [PubMed] [Article]
31. Kollmorgen, S., Nortmann, N., Schräoder, S., & Käonig, P. (2010). Influence of low-level stimulus features, task dependent factors, and spatial biases on overt visual attention. PLoS Computational Biology, 6, 2010.
32. Krieger, G., Rentschler, I., Hauske, G., Schill, K., & Zetzsche, C. (2000). Object and scene analysis by saccadic eye movements: An investigation with higher-order statistics. Spatial Vision, 13, 201-214.
33. Lang, P., Bradley, M., & Cuthbert, B. (2008). (IAPS): Affective ratings of pictures and instruction manual. Technical Report, University of Florida.
34. Le Meur, O., Le Callet, P., Barba, D., & Thoreau, D. (2006). A coherent computational approach to model the bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 802-817.
35. Li, Z. (2002). A saliency map in primary visual cortex. Trends in Cognitive Sciences, 6, 9-16.
36. Mannan, S., Kennard, C., & Husain, M. (2009). The role of visual salience in directing eye movements in visual object agnosia. Current Biology, 19, 247-248.
37. Mannan, S., Ruddock, K. H., & Wooding, D. S. (1997). Fixation patterns made during visual examination of briefly presented 2-D images. Perception, 26, 1059-1072.
38. Masciocchi, C., Mihalas, S., Parkhurst, D., & 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]
39. Motter, B. C., & Belky, E. J. (1998). The guidance of eye movements during active visual search. Vision Research, 38, 1805-1815.
40. Navalpakkam, V., & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45, 205-231.
41. Nothdurft, H. (2000). Salience from feature contrast: Additivity across dimensions. Vision Research, 40, 1183-1201.
42. Oliva, A., Torralba, A., Castelhano, M., & Henderson, J. (2003). Top-down control of visual attention in object detection. In International Conference on Image Processing (vol. I, pp. 253-256). IEEE Computer Society.
43. Pare, M., & Munoz, D. (2001). Expression of a recentering bias in saccade regulation by superior colliculus neurons. Experimental Brain Research, 137, 354-368.
44. Parkhurst, D., Law, K., & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42, 107-123.
45. Pelz, J. B., & Canosa, R. (2001). Oculomotor behavior and perceptual strategies in complex tasks. Vision Research, 41, 3587-3596.
46. Peters, R., Iyer, A., Itti, L., & Koch, C. (2005). Components of bottom-up gaze allocation in natural images. Vision Research, 45, 2397-2416.
47. Rajashekar, U., Bovik, A. C., & Cormack, L. K. (2006). Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis. Journal of Vision, 6(4):7, 379-386, http://www.journalofvision.org/content/6/4/7, doi:10.1167/6.4.7.
48. Rajashekar, U., van der Linde, I., Bovik, A. C., & Cormack, L. K. (2008). GAFFE: A gaze-attentive fixation finding engine. IEEE Transactions on Image Processing, 17, 564-573.
49. Rao, R. P., Zelinsky, G. J., Hayhoe, M. M.,& Ballard, D. H. (2002). Eye movements in iconic visual search. Vision Research, 42, 1447-1463.
50. Reinagel, P., & Zador, A. (1999). Natural scene statistics at the centre of gaze. Network, 10, 341-350.
51. Rubner, Y., Tomasi, C., & Guibas, L. (2000). The earth movers distance as a metric for image retrieval. International Journal of Computer Vision, 40, 99-121.
52. Rutishauser, U., & Koch, C. (2007). Probabilistic modeling of eye movement data during conjunction search via feature-based attention. Journal of Vision, 7(6):5, 1-20, http://www.journalofvision.org/content/7/6/5, doi:10.1167/7.6.5.
53. Subramanian, R., Katti, H., Sebe, N., Kankanhalli, M., & Chua, T. S. (2010). An eye fixation database for saliency detection in images. In European Conference on Computer Vision (vol. 6314, pp. 30-43). Springer.
54. Tatler, B. (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.
55. Tatler, B., Baddeley, R., & Gilchrist, I. (2005). Visual correlates of fixation selection: Effects of scale and time. Vision Research, 45, 643-659.
56. Torralba, A., Oliva, A., Castelhano, M.,&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, 766-786.
57. Treisman, A. M., & Gelade, G. (1980). A featureintegration theory of attention. Cognitive Psychology, 12, 97-136.
58. Underwood, G., & Foulsham, T. (2006). Visual saliency and semantic incongruency influence eye movements when inspecting pictures. Quarterly Journal of Experimental Psychology, 59, 1931-1949.
59. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition (vol. I, pp. 511-518). IEEE Computer Society.
60. Vitu, F., Kapoula, Z., Lancelin, D., & Lavigne, F. (2004). Eye movements in reading isolated words: Evidence for strong biases towards the center of the screen. Vision Research, 44, 321-338.
61. Walther, D., Serre, T., Poggio, T., & Koch, C. (2005). Modeling feature sharing between object detection and top-down attention [Abstract]. Journal of Vision, 5(8):1041, 1041a, http://www.journalofvision.org/content/5/8/1041, doi:10.1167/5.8.1041.
62. Williams, D. E., & Reingold, E. M. (2001). Preattentive guidance of eye movements during triple conjunction search tasks: The effects of feature discriminability and saccadic amplitude. Psychonomic Bulletin & Review, 8, 476-488.
63. Williams, L. G. (1966). Effect of target specification on objects fixated during visual search. Perception & Psychophysics, 1, 315-318.
64. Zambarbieri, D., Beltrami, G., & Versino, M. (1995). Saccade latency toward auditory targets depends on the relative position of the sound source with respect to the eyes. Vision Research, 35, 3305-3312.
65. Zelinsky, G. J. (1996). Using eye saccades to assess the selectivity of search movements. Vision Research, 36, 2177-2187.
66. Zhang, L., Tong, M., & Cottrell, G. (2009). SUNDay: Saliency using natural statistics for dynamic analysis of scenes. In Proceedings of the 31st Annual Cognitive Science Conference (pp. 2944-2949). Curran Associates, Inc.
67. Zhang, L., Tong, M., Marks, T., Shan, H., & Cottrell, G. (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.