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Hierarchical Selectivity for Object-Based Visual Attention

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Biologically Motivated Computer Vision (BMCV 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2525))

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Abstract

This paper presents a novel “hierarchical selectivity” mechanism for object-based visual attention. This mechanism integrates visual salience from bottom-up groupings and the top-down attentional setting. Under its guidance, covert visual attention can shift not only from one grouping to another but also from a grouping to its sub-groupings at a single resolution or multiple varying resolutions. Both object-based and space-based selection is integrated to give a visual attention mechanism that has multiple and hierarchical selectivity.

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References

  1. S. Baluja, and D. Pomerleau, “Dynamic relevance: Vision-based focus of attention using artificial neural networks,” Artificial Intelligence, 97, pp. 381–395, 1997.

    Article  MATH  Google Scholar 

  2. M. Behrmann, R. S. Zemel, and M. C. Mozer, “Occlusion, symmetry, and objectbased attention: reply to Saiki (2000),” Journal of Experimental Psychology: Human Perception and Performance, 26(4), pp. 1497–1505, 2000.

    Article  Google Scholar 

  3. C. Koch and S. Ullman, “Shifts in selective visual attention: towards the underlying neural circuity,” Human Neurobiology, 4:481–484, 1985.

    Google Scholar 

  4. R. Desimone, and J. Duncan, “Neural mechanisms of selective visual attention,” Ann. Rev. Neurosci., 18, pp. 193–222, 1995.

    Article  Google Scholar 

  5. R. Desimone, “Visual attention mediated by biased competition in extrastriate visual cortex,” Phil. Trans. R. Soc. Lond. B, 353, pp. 1245–1255, 1998.

    Article  Google Scholar 

  6. J. Duncan, “Converging levels of analysis in the cognitive neuroscience of visual attention,” Phil. Trans. R. Soc. Lond. B., 353, pp. 1307–1317, 1998.

    Article  Google Scholar 

  7. H. Greenspan, S. Belongie, R. Goodman, P. Persona, S. Rakshit, and C. H. Anderson, “Overcomplete steerable pyramid filters and rotation invariance,” In proc. IEEE Computer Vision and Pattern Recognition, pp. 222–228, Seattle, Washington, 1994.

    Google Scholar 

  8. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), pp. 1254–1259, 1998.

    Article  Google Scholar 

  9. L. Itti and C. Koch, “Salience-Based serach mechanism for overt and covert shifts of visual attention,” 40(10–12):1489–1506, 2000.

    Google Scholar 

  10. S. Kastner and L. G. Ungerleider, “Mechanisms of visual attention in the human cortex,” Annu. Rev. Neurosci., 23:315–341, 2002.

    Google Scholar 

  11. S. E. Palmer, Vision Science-Photons to Phenomenology, Cambridge, MA: MIT Press, 1999.

    Google Scholar 

  12. B. J. Scholl, “Objects and attention: the state of the art,” Cognition, 80, pp. 1–46, 2001.

    Article  Google Scholar 

  13. J. K. Tsotsos, et al. “Modelling visual attention via selective tuning,” Artificial Intelligence, 78, pp. 507–545, 1995.

    Article  MathSciNet  Google Scholar 

  14. Yaoru Sun and Robert Fisher, “Object-based visual attention for computer vision,” submitted to Artificial Intelligence.

    Google Scholar 

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© 2002 Springer-Verlag Berlin Heidelberg

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Sun, Y., Fisher, R. (2002). Hierarchical Selectivity for Object-Based Visual Attention. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_43

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  • DOI: https://doi.org/10.1007/3-540-36181-2_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00174-4

  • Online ISBN: 978-3-540-36181-7

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