Effectiveness analysis of selected attention models

• Autorzy: Włodarczyk M. , Kryjak T. , Wolski P.
• Języki publikacji: EN

Abstrakty

EN

The aim of this study was an empirical verification of five different computer models of exogenous human attention. These models allow to predict the likely fixations on a static image and calculate saliency maps which show how distinctive the image elements are. The basis for the verification of the models was a modified cuing task , originally developed by Posner. Using the algorithms provided by the authors of the models an analysis of selected images was performed. Then the effectiveness of the models was verified in experimental studies: an image was presented to the subject, then on this image a visual stimulus appeared and the reaction time was measured. It was assumed that the emergence of a stimulus at the point where previously an attention attracting element appeared would accelerate the reaction. The study was conducted on a set of over 300 different, emotionally neutral, natural images. Obtained results indicate the potential usefulness of the proposed schema for testing the efficiency and refinement of attention models.

Słowa kluczowe

EN

attention modeling; saliency; Posner's paradigm

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 1
• Strony: 49--56
• Opis fizyczny: Bibliogr. 12 poz., zdj., tab., wykr.

Twórcy

autor

Włodarczyk M.

• Multidisciplinary School of Engineering in Biomedicine, AGH-UST Krakow

autor

Kryjak T.

• Department of Automatics, Laboratory of Biocybernetics, AGH-UST Krakow

autor

Wolski P.

• Institute of Psychology, Jagiellonian Univeristy, Krakow

Bibliografia

• [1] Itti L., Koch C., Niebur E.: "A model of saliency-based visual attention for rapid scene analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20 no. 11, pp. 1254-1259, 1998.
• [2] Itti. L., Koch C.: "Computational modelling of visual attention." Nature reviews. Neuroscience, vol. 2, no. 3, pp. 194-203, 2001.
• [3] Zhang L., Tong M. H., Marks T. K., Shan H., Cottrell, G. W.: "SUN: A Bayesian framework for saliency using natural statistic". Journal of Vision, vol. 8, no. 7:32, pp. 1-20, 2008.
• [4] Posner, M. I., Cohen, Y. "Components of visual orienting”. W: H. Bouma & D. G. Bouwhuis (Eds.), Attention and performance X. Hillsdale, NJ: Erlbaum, pp. 531–555, 1984.
• [5] Butko N., Zhang L., Cottrel G. W., Movellan J. R., "Visual Saliency Model for Robot Cameras", International Conference on Robotics and Automation, 2008.
• [6] http://www.saliencytoolbox.net/
• [7] Zhai Y., Shah M. "Visual attention detection in video sequences using spatiotemporal cues". In Proceedings of the 14th Annual ACM international Conference on Multimedia, Santa Barbara, USA, 2006.
• [8] Bruce N., Tsotos J. "Saliency, attention, and visual search: An information theoretic approach", Journal of Vision, vol. 9, no.3, pp. 1-24, 2009.
• [9] http://www.u.arizona.edu/~kforster/dmdx/dmdx.htm
• [10] Tadeusiewicz R., Korohoda P.: "Komputerowa analiza i przetwarzanie obrazów", Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków, 1997 (in Polish).
• [11] Corbeta, M., Shulman, G.L. "Control of goal-directed and stimulus-driven attention in the brain" Nature Reviews Neuroscience, 3, 201-215, 2002.
• [12] Peters, J.R., Iyer, A., Itti, L., Koch, C. "Components of bottom-up gaze allocation in natural images." Vision Research, 45, 2397-2416, 2005.