Saliency and human fixations: State-of-the-art and study of comparison metrics

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 1153-1160

  Riche, Nicolas ^a   Duvinage, Matthieu ^a   Mancas, Matei ^a   Gosselin, Bernard ^a   Dutoit, Thierry ^a  

^a UNIVERSITY OF MONS   (Belgium)

Author keywords

Human eye fixations; Metrics; Saliency maps; Validation

Indexed keywords

COMPUTER SCIENCE; ELECTRICAL ENGINEERING;

COMPARISON METRICS; HUMAN EYE; METRICS; PERFORMANCE METRICS; SALIENCY MAP; SALIENCY MODELING; SIMILARITY METRICS; VALIDATION;

COMPUTERS;

EID: 84898774374     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.147     Document Type: Conference Paper

References (32)

1. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In 2009, IEEE Conference on CVPR, pages 1597-1604. IEEE, 2009.
2. U. Attention Group. Computational Attention Website. http://tcts.fpms.ac. be/attention.
3. A. Borji. Evaluation measures for saliency maps. https://sites.google. com/site/saliencyevaluation/home.
4. A. Borji and L. Itti. State-of-the-art in visual attention modeling. IEEE Transactions on PAMI, 2012.
5. A. Borji, D. Sihite, and L. Itti. Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE TIP, 2012.
6. N. Bruce and J. Tsotsos. Saliency based on information maximization. Advances in neural information processing systems, 18:155, 2006.
7. A. Garcia-Diaz, X. Fdez-Vidal, X. Pardo, and R. Dosil. Decorrelation and distinctiveness provide with human-like saliency. In Advanced Concepts for Intelligent Vision Systems, pages 343-354. Springer, 2009.
8. C. Guo, Q. Ma, and L. Zhang. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In 2008, IEEE Conference on CVPR, pages 1-8. IEEE, 2008.
9. X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In 2007, IEEE Conference on CVPR, pages 1-8. IEEE, 2007.
10. X. Hou and L. Zhang. Dynamic visual attention: Searching for coding length increments. Advances in neural information processing systems, 21:681-688, 2008.
11. D. C. Howell. Statistical methods for psychology. Wadsworth Publishing Company, 2012.
12. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on PAMI, 20(11):1254-1259, 1998.
13. T. Judd. saliency benchmark. http://people.csail. mit.edu/tjudd/ SaliencyBenchmark/.
14. T. Judd, F. Durand, and A. Torralba. A benchmark of computational models of saliency to predict human fixations. MIT tech report, 2012.
15. C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol, 4(4):219-27, 1985.
16. O. Le Meur and T. Baccino. Methods for comparing scanpaths and saliency maps: strengths and weaknesses. Behavior Research Methods, pages 1-16, 2011.
17. O. Le Meur, P. Le Callet, D. Barba, et al. Predicting visual fixations on video based on low-level visual features. Vision research, 47(19):2483-2498, 2007.
18. J. Li, M. D. Levine, X. An, X. Xu, and H. He. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on PAMI, 35:996-1010, 2012.
19. N. Ouerhani, R. Von Wartburg, H. Hugli, and R. Muri. Empirical validation of the saliency-based model of visual attention. Electronic letters on computer vision and image analysis, 3(1):13-24, 2004.
20. O. Pele and M. Werman. A linear time histogram metric for improved sift matching. In Computer Vision-ECCV 2008, pages 495-508. Springer, 2008.
21. O. Pele and M. Werman. Fast and robust earth mover's distances. In 2009 IEEE 12th ICCV, pages 460-467. IEEE, 2009.
22. R. J. Peters and L. Itti. Applying computational tools to predict gaze direction in interactive visual environments. ACM Transactions on Applied Perception (TAP), 5(2):9, 2008.
23. R. J. Peters, A. Iyer, L. Itti, and C. Koch. Components of bottom-up gaze allocation in natural images. Vision research, 45(18):2397-2416, 2005.
24. U. Rajashekar, L. K. Cormack, and A. C. Bovik. Point-ofgaze analysis reveals visual search strategies. In Proceedings of SPIE, volume 5292, pages 296-306, 2004.
25. N. Riche, M. Mancas, M. Duvinage, M. Mibulumukini, B. Gosselin, and T. Dutoit. Rare2012: A multi-scale raritybased saliency detection with its comparative statistical analysis. Signal Processing: Image Communication, 28(6):642-658, 2013.
26. B. W. Tatler, R. J. Baddeley, I. D. Gilchrist, et al. Visual correlates of fixation selection: Effects of scale and time. Vision research, 45(5):643-659, 2005.
27. A. Toet. Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE Transactions on PAMI, 33(11):2131-2146, 2011.
28. A. Torralba. Modeling global scene factors in attention. JOSA A, 20(7):1407-1418, 2003.
29. A. Torralba, A. Oliva, M. S. Castelhano, and J. M. Henderson. Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychological review, 113(4):766, 2006.
30. J. K. Tsotsos, S. M. Culhane,W. Y. KeiWai, Y. Lai, N. Davis, and F. Nuflo. Modeling visual attention via selective tuning. Artificial intelligence, 78(1):507-545, 1995.
31. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G.W. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 8(7), 2008.
32. Q. Zhao and C. Koch. Learning a saliency map using fixated locations in natural scenes. Journal of vision, 11(3), 2011.