Saliency revisited: Analysis of mouse movements versus fixations

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 6354-6362

  Tavakoli, Hamed R ^a   Ahmed, Fawad ^b   Borji, Ali ^b   Laaksonen, Jorma ^a  

^a AALTO UNIVERSITY   (Finland)

^b University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; EYE MOVEMENTS; EYE TRACKING; MAMMALS;

CONTEXTUAL FACTORS; CONTEXTUAL INFORMATION; EVALUATION METHODOLOGIES; EVALUATION SCHEME; MODEL SELECTION; OBJECT ATTRIBUTES; QUANTITATIVE APPROACH; QUANTITATIVE EVALUATION;

QUALITY CONTROL;

EID: 85041901191     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.673     Document Type: Conference Paper

References (36)

1. A. Borji and L. Itti. State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell., 35(1):185-207, 2013.
2. A. Borji and L. Itti. Cat2000: A large scale fixation dataset for boosting saliency research. arXiv:1505.03581, 2015.
3. A. Borji, D. Sihite, and L. Itti. Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Trans. Image Process., 22(1):55-69, 2013.
4. A. Borji, H. R. Tavakoli, D. N. Sihite, and L. Itti. Analysis of scores, datasets, and models in visual saliency prediction. In ICCV, 2013.
5. Z. Bylinskii, P. Isola, C. Bainbridge, A. Torralba, and A. Oliva. Intrinsic and extrinsic effects on image memorability. Vision Res., 116, Part B:165-178, 2015.
6. Z. Bylinskii, T. Judd, A. Borji, L. Itti, F. Durand, A. Oliva, and A. Torralba. Mit saliency benchmark. Online, 2016.
7. Z. Bylinskii, T. Judd, A. Oliva, A. Torralba, and F. Durand. What do different evaluation metrics tell us about saliency models? arXiv:1604.03605, 2016.
8. Z. Bylinskii, A. Recasens, A. Borji, A. Oliva, A. Torralba, and F. Durand. Where should saliency models look next? In ECCV, 2016.
9. E. Erdem and A. Erdem. Visual saliency estimation by non-linearly integrating features using region covariances. J Vis, 13(4), 2013.
10. A. Garcia-Diaz, X. Fdez-Vidal, X. Pardo, and R. Dosil. Decorrelation and distinctiveness provide with human-like saliency. In ACIVS, 2009.
11. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. In NIPS, 2007.
12. X. Huang, C. Shen, X. Boix, and Q. Zhao. Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks. In ICCV, 2015.
13. L. Itti and P. Baldi. Bayesian surprise attracts human attention. Vision Res., 49(10):1295 - 1306, 2009.
14. M. Jiang, S. Huang, J. Duan, and Q. Zhao. SALICON: Saliency in context. In CVPR, 2015.
15. M. Jiang, J. Xu, and Q. Zhao. Saliency in crowd. In ECCV, 2014.
16. T. Jost, N. Ouerhani, R. von Wartburg, R. Müri, and H. Hügli. Assessing the contribution of color in visual attention. Comput. Vis. Image Und., 100(12):107 - 123, 2005.
17. T. Judd, F. Durand, and A. Torralba. A benchmark of computational models of saliency to predict human fixations. Technical Report MIT-CSAIL-TR-2012-001, Massachusetts institute of technology, 2012.
18. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In ICCV, 2009.
19. G. Kootstra, B. de Boer, and L. R. B. Schomaker. Predicting eye fixations on complex visual stimuli using local symmetry. Cog. Comp., 3(1):223-240, 2011.
20. M. Kümmerer, T. S. A. Wallis, and M. Bethge. Information-theoretic model comparison unifies saliency metrics. Proc. Natl. Acad. Sci. U.S.A., 112(52):16054-16059, 2015.
21. O. Le Meur and T. Baccino. Methods for comparing scan-paths and saliency maps: strengths and weaknesses. Behav Res Methods, 45(1):251-266, 2013.
22. Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille. The secrets of salient object segmentation. In CVPR, 2014.
23. J. Pan, K. McGuinness, E. Sayrol, N. O'Connor, and X. Giro-i Nieto. Shallow and deep convolutional networks for saliency prediction. In CVPR, 2016.
24. R. J. Peters, A. Iyer, L. Itti, and C. Koch. Components of bottom-up gaze allocation in natural images. Vision Res., 45:2397-2416, 2005.
25. S. Ramanathan, H. Katti, N. Sebe, M. Kankanhalli, and T.S. Chua. An eye fixation database for saliency detection in images. In ECCV, 2010.
26. N. Riche, M. Duvinage, M. Mancas, B. Gosselin, and T. Du-toit. Saliency and human fixations: State-of-the-art and study of comparison metrics. In ICCV, 2013.
27. C. Thomas. Opensalicon: An open source implementation of the salicon saliency model. arxiv:1606.00110, 2016.
28. A. Toet. Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE Trans. Pattern Anal. Mach. Intell., 33(11):2131-2146, 2011.
29. A. Torralba, A. Oliva, M. Castelhano, and J. Henderson. Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychol Rev, 113(4):766-86, 2006.
30. B. Velichkovsky, M. Pomplun, and J. Rieser. Attention and communication: Eye-movement-based research paradigms. In W. Zangemeister, H. Stiehl, and C. Freksa, editors, Visual Attention and Cognition, volume 116 of Advances in Psychology, pages 125 - 154. North-Holland, 1996.
31. E. Vig, M. Dorr, and D. Cox. Large-scale optimization of hierarchical features for saliency prediction in natural images. In CVPR, 2014.
32. C. Wloka and J. Tsotsos. Spatially binned roc: A comprehensive saliency metric. In CVPR, 2016.
33. J. Xu, M. Jiang, S. Wang, M. S. Kankanhalli, and Q. Zhao. Predicting human gaze beyond pixels. J Vis, 14(1):1-20, 2014.
34. P. Xu, K. A. Ehinger, Y. Zhang, A. Finkelstein, S. R. Kulka-rni, and J. Xiao. Turkergaze: Crowdsourcing saliency with webcam based eye tracking. In arXiv:1504.06755v1, 2015.
35. J. Zhang and S. Sclaroff. Saliency detection: A boolean map approach. In ICCV, 2013.
36. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cot-trell. Sun: A bayesian framework for saliency using natural statistics. J Vis, 8(7), 2008.