Studying relationships between human gaze, description, and computer vision

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 739-746

  Yun, Kiwon ^a   Peng, Yifan ^a   Samaras, Dimitris ^a   Zelinsky, Gregory J ^b   Berg, Tamara L ^a  

^a Stony Brook University   (United States)

^b STONY BROOK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN CUES; NATURAL LANGUAGES; USER BEHAVIORS; VISUAL RECOGNITION;

EYE MOVEMENTS; PATTERN RECOGNITION;

BEHAVIORAL RESEARCH;

EID: 84887396648     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.101     Document Type: Conference Paper

References (35)

1. P. F. Baldi and L. Itti. Of bits and wows: A bayesian theory of surprise with applications to attention. Neural Networks, 23(5):649-666, 2010. 2
2. A. C. Berg, T. L. Berg, H. Daume, J. Dodge, A. Goyal, X. Han, A. Mensch, M. Mitchell, A. Sood, K. Stratos, et al. Understanding and predicting importance in images. In CVPR, pages 3562-3569. IEEE, 2012. 5
3. M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky. Exploiting hierarchical context on a large database of object categories. In CVPR, 2010. 2, 3
4. T. De Campos, G. Csurka, and F. Perronnin. Images as sets of locally weighted features. Computer Vision and Image Understanding, 116(1):68-85, 2012. 2
5. J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and F.-F. Li. Large scale visual recognition challenge. In http://www. image-net. org/challenges/LSVRC/2012/index, 2012. 1
6. J. Deng, A. C. Berg, K. Li, and F.-F. Li. What does classifying more than 10,000 image categories tell us? In ECCV, 2010. 1
7. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009. 3
8. K. A. Ehinger, B. Hidalgo-Sotelo, A. Torralba, and A. Oliva. Modelling search for people in 900 scenes: A combined source model of eye guidance. Visual Cognition, 17(6):945-978, 2009. 2
9. W. Einhäuser, M. Spain, and P. Perona. Objects predict fixations better than early saliency. Journal of Vision, 8(14), 2008. 2
10. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results. 2, 3
11. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. 1
12. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. TPAMI, pages 1627-1645, 2009. 1, 3, 6
13. J. M. Henderson. Human gaze control during real-world scene perception. Trends in cognitive sciences, 7(11):498-504, 2003. 6
14. L. Itti. Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. Vision Cognition, 12:1093-1123, 2005. 2
15. L. Itti and P. F. Baldi. Bayesian surprise attracts human attention. Vision Research, 49(10):1295-1306, 2009. 2
16. L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40:1489-1506, 2000. 2
17. L. Itti and C. Koch. Computational modeling of visual attention. Nature Reviews Neuroscience, 2(3):194-203, 2001. 2
18. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 1
19. Y. Lin, F. Lv, S. Zhu, M. Yang, T. Cour, K. Yu, L. Cao, and T. Huang. Large-scale image classification: Fast feature extraction and svm training. In CVPR, 2011. 1
20. N. Mackworth and A. Morandi. The gaze selects informative details within pictures. Perception and Psychophysics, 2:547-552, 1967. 2
21. M. B. Neider and G. J. Zelinsky. Scene context guides eye movements during search. Vision Research, pages 614-621, 2006. 2
22. M. B. Neider and G. J. Zelinsky. Searching for camouflaged targets: Effects of target-background similarity on visual search. Vision Research, 46:2217-2235, 2006. 2
23. D. J. Parkhurst, K. Law, and E. Niebur. Modeling the role of salience in the allocation of overt visual selective attention. Vision Research, 42:107-123, 2002. 2
24. F. Perronnin, Z. Akata, Z. Harchaoui, and C. Schmid. Towards good practice in large-scale learning for image classification. In CVPR, 2012. 1
25. X.-H. Phan. CRFTagger: CRF English POS Tagger. http://crftagger. sourceforge. net/, 2006. 5
26. C. Rashtchian, P. Young, M. Hodosh, and J. Hockenmaier. Collecting image annotations using amazon's mechanical turk. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, 2010. 3
27. L. W. Renninger, P. Vergheese, and J. Coughlan. Where to look next? eye movements reduce local uncertainty. Journal of Vision, 3:1-17, 2007. 2
28. B. W. Tatler. 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):1-17, 2007. 2
29. B. W. Tatler, R. J. Baddeley, and B. T. Vincent. The long and the short of it: Spatial statistics at fixation vary with saccade amplitude and task. Vision Research, 46:1857-1862, 2006. 2
30. J. Theeuwes. Stimulus-driven capture and attentional set: selective search for color and visual abrupt onsets. Journal of Experimental Psychology: Human Perception and Performance, 20:799-806, 1994. 2
31. J. Theeuwes, A. Kramer, S. Hahn, D. Irwin, and G. Zelinsky. Influence of attentional capture on oculomotor control. Journal of Experimental Psychology: Human Perception and Performance, 25:1595-1608, 1999. 2
32. Z. Wu and M. Palmer. Verbs semantics and lexical selection. In ACL, pages 133-138, Stroudsburg, PA, USA, 1994. 5
33. A. Yarbus. Eye movements and vision. Plenum Press, 1967. 2
34. G. J. Zelinsky. A theory of eye movements during target acquisition. Psychological Review, 115(4):787-835, 2008. 2
35. G. J. Zelinsky and J. Schmidt. An effect of referential scene constraint on search implies scene segmentation. Visual Cognition, 17(6):1004-1028, 2009. 2