Abstract
Visual attention is considered nowadays a paramount ability both in Cognitive Sciences and in Cognitive Vision to bridge the gap between perception and higher level reasoning functions, such as scene interpretation and decision making. Bottom-up gaze shifting is the main mechanism used by humans when exploring a scene without a specific task. In this paper we investigated which criteria allow for the generation of plausible fixation clusters by analysing experimental data of human subjects. We suggest that fixations should be grouped in cliques whose saliency can be assessed through an innovation factor encompassing bottom-up cues, proximity, direction and memory components.
References
Belardinelli A, Pirri F, Carbone A (2006) Spatial discrimination in task-driven attention. In: Proceedings of IEEE RO-MAN’06. Hatfield, UK, pp 321–327
Belardinelli A, Pirri F, Carbone A (2007) Bottom-up gaze shifts and fixations learning by imitation. IEEE Trans Syst Man Cybern B 37:256–271
Bishop CM (2006) Pattern recognition and machine learning. Springer, Heidelberg
Bruce NDB, Tsotsos JK (2006) Saliency based on information maximization. Adv Neural Inf Process Syst 18:155–162
Findlay JM, Brown V (2006) Eye scanning of multi-element displays: I scanpath planning. Vis Res 46:179–195
Frintrop S, Jensfelt P, Christensen H (2006) Attentional landmark selection for visual slam. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS’06)
Härdle W, Hlavka Z (2007) Multivariate statistics: exercises and solutions. Springer, Heidelberg
Itti L, Baldi P (2006) Bayesian surprise attracts human attention. In: Advances in neural information processing systems, vol 19 (NIPS*2005). MIT Press, Cambridge, pp 1–8
Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2(3):194–203
Just M, Carpenter P (1980) A theory of reading. Psychol Rev 87:329–354
Klein RM (2000) Inhibition of return. Trends Cogn Sci 4:138–147
Kramer AF, Wiegmann DA, Kirlik A (2007) Attention. From theory to practice. Oxford University Press, Oxford
Mardia K, Kent J, Bibby J (1979) Multivariate analysis. Academic Press, London
Najemnik J, Geisler WS (2005) Optimal eye movement strategies in visual search. Nature 434:387–391
Posner MI (1980) Orienting of attention. Q J Exp Psychol 32-A:3–25
Raj R, Geisler WS, Frazor RA, Bovik AC (2005) Contrast statistics for foveated visual systems: fixation selection by minimizing contrast entropy. J Opt Soc Am 22(10):2039–2049
Renninger LW, Coughlan J, Verghese P, Malik J (2005) An information maximization model of eye movements. Adv Neural Inf Process Syst 17:1121–1128
Santella A, Decarlo D (2003) Robust clustering of eye movement recordings for quantification of visual interest. In ETRA 2004. New York, pp 23–34
Shokoufandeh A, Sala PL, Sim R, Dickinson SJ (2006) Landmark selection for vision-based navigation. IEEE Trans Rob 22(2):334–349
Spalek TM, Hammad S (2004) Supporting the attentional momentum view of ior: Is attention biased to go right? Percept Psychophys 66(2):219–233
Thibadeau R, Just M, Carpenter P (1980) Real reading behaviour. In: Proceedings of the 18th annual meeting on association for computational linguistics, Morristown, NJ, USA. Association for Computational Linguistics, pp 159–162
Treisman A, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12:97–136
Tsotsos JK, Culhane S, Wai W, Lai Y, Davis N, Nuflo F (1995) Modeling visual attention via selective tuning. Artif Intell 78:507–547
Turano KA, Geruschat DR, Baker FH (2003) Oculomotor strategies for the direction of gaze tested with a real-world activity. Vis Res 43:333–346
Yarbus AL (1967) Eye movements and vision. Plenum Press, New York
Zhang Z (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: The Proceedings of the seventh IEEE international conference on Computer vision, 1999, vol 1, pp 666–673
Acknowledgments
The authors would like to thank the reviewers for their worthwhile suggestions. This research has been supported by the European Union 6th Framework Programme Project Viewfinder.
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Belardinelli, A., Pirri, F. & Carbone, A. Gaze motion clustering in scan-path estimation. Cogn Process 9, 269–282 (2008). https://doi.org/10.1007/s10339-008-0206-2
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DOI: https://doi.org/10.1007/s10339-008-0206-2