Abstract
Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.
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Tsotsos, J.: Analyzing vision at the complexity level. Behavioral and Brain Sciences 13, 423–445 (1990)
Wolfe, J.M., Cave, K.: Deploying visual attention: The guided search model. John Wiley and Sons Ltd., Chichester (1990)
Tsotsos, J.K., Culhane, S.M., Wai, W.: Modeling visual attention via selective tuning. Artificial Intelligence 78, 507–545 (1995)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (1998)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)
Yang, Y., Song, M., Li, N., Bu, J., Chen, C.: Visual attention analysis by pseudo gravitational field. ACM Multimedia, 553–556 (2009)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: International Conference on Computer Vision, Acceptance (2009)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Bruce, N.D.B.: Features that draw visual attention: An information theoretic perspective. Neurocomputing 65-66, 125–133 (2005)
Bruce, N.D.B., Tsotsos, J.K.: Saliency based on information maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Advances in neural information processing systems 19, 547–554 (2006)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8, 1–20 (2008)
Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24
Meur, O.L., Callet, P.L., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Trans on Pattern Analysis and Machine Intelligence 28, 802–816 (2006)
Desolneux, A., Moisan, L., Morel, J.: From Gestalt Theory to Image Analysis, A Probabilistic Approach. Springer Science and Business Media, Heidelberg (2008)
Liu, H., Jiang, S., Huang, Q., Xu, C., Gao, W.: Region-based visual attention analysis with its application in image browsing on small displays. ACM Multimedia (2007)
Zhai, G., Chen, Q., Yang, X., Zhang, W.: Scalable visual sensitivity profile estimation. In: ICASSP, pp. 876–879 (2008)
de Wouwer, G.V., Scheunders, P., Dyck, D.V.: Statistical texture characterization from discrete wavelet representations. IEE Trans. Image Processing 8, 592–598 (1999)
Poor, H.V.: An Introduction to Signal Estimation and Detection, 2nd edn. Springer, Heidelberg (1994)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
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Yang, Y., Song, M., Li, N., Bu, J., Chen, C. (2010). What Is the Chance of Happening: A New Way to Predict Where People Look. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_46
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DOI: https://doi.org/10.1007/978-3-642-15555-0_46
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