Abstract
We propose a method to predict human saccadic scanpaths on natural images based on a bio-inspired visual attention model. The method integrates three related factors as driven forces to guide eye movements, sequentially-visual saliency, winner-takes-all and visual memory, respectively. When predicting a current fixation of saccadic scanpaths, we follow physiological visual memory characteristics to eliminate the effects of the previous selected fixation. Then, we use winner-takes-all to select the fixation on the current saliency map. Experimental results demonstrate that the proposed model outperform other methods on both static fixation locations and dynamic scanpaths.
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References
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. TIP 13(10), 1304–1318 (2004)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. TPAMI 20(11), 1254–1259 (1998)
Gao, D., Han, S., Vasconcelos, N.: Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. TPAMI 31(6), 989–1005 (2009)
Klarquist, W., Bovik, A.: Fovea: a foveated vergent active stereo vision system for dynamicthree-dimensional scene recovery. IEEE Transactions on Robotics and Automation 14(5), 755–770 (1998)
Osberger, W., Bergmann, N., Maeder, A.: An automatic image quality assessment technique incorporating higher level perceptual factors. In: Proceedings of the 1998 International Conference on Image Processing, ICIP 1998, vol. 3, pp. 414–418 (1998)
Lee, S., Pattichis, M., Bovik, A.: Foveated video compression with optimal rate control. IEEE Transactions on Image Processing 10(7), 977–992 (2001)
Wang, Z., Lu, L., Bovik, A.: Foveation scalable video coding with automatic fixation selection. IEEE Transactions on Image Processing 12(2), 243–254 (2003)
Yang, G.-Z., Dempere-Marco, L., Hu, X.-P., Rowe, A.: Visual search: psychophysical models and practical applications. Image and Vision Computing 20(4), 273–287 (2002)
Privitera, C., Stark, L.: Human-vision-based selection of image processing algorithms for planetary exploration. IEEE Transactions on Image Processing 12(8), 917–923 (2003)
Tsotsos, J.K., Culhane, S.M., Kei Wai, W.Y., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78(1), 507–545 (1995)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 473–480 (2011)
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© 2014 Springer International Publishing Switzerland
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Duan, L., Qiao, H., Wu, C., Yang, Z., Ma, W. (2014). Modeling of Human Saccadic Scanpaths Based on Visual Saliency. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_28
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DOI: https://doi.org/10.1007/978-3-319-01796-9_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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