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Evaluation of the Impetuses of Scan Path in Real Scene Searching

  • Chen Chi
  • Laiyun Qing
  • Jun Miao
  • Xilin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

The modern computer vision systems usually scan the image over positions and scales to detect a predefined object, whereas the human vision system performs this task in a more intuitive and efficient manner by selecting only a few regions to fixate on. A comprehensive understanding of human search will benefit computer vision systems in search modeling. In this paper, we investigate the contributions of the sources that affect human eye scan path while observers perform a search task in real scenes. The examined sources include saliency, task guidance, and oculomotor bias. Both their influence on each consecutive pair fixations and on the entire scan path are evaluated. The experimental results suggest that the influences of task guidance and oculomotor bias are comparable, and that of saliency is rather low. They also show that we could use these sources to predict not only where humans look in the image but also the order of their visiting.

Keywords

Edit Distance Computer Vision System Combine Source Saccade Length Task Guidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Viola, P., Jones, M.J.: Robust real time object detection. In: Workshop on Statistical and Computational Theories of Vision (2001)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  3. 3.
    Ehinger, K., Hidalgo-Sotelo, B., Torralba, A., Oliva, A.: Modelling search for people in 900 scenes: A combined source model of eye guidance. Visual Cognition 17, 945–978 (2009)CrossRefGoogle Scholar
  4. 4.
    Yarbus, A.: Eye movements and vision. Plenum press, New York (1967)CrossRefGoogle Scholar
  5. 5.
    Rayner, K.: Eye movements in reading and information processing. Psychological Bulletin 85, 618–660 (1978)CrossRefGoogle Scholar
  6. 6.
    Robinson, D.: The mechanics of human saccadic eye movement. The Journal of Physiology 174, 245 (1964)CrossRefGoogle Scholar
  7. 7.
    Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: CVPR, pp. 2049–2056 (2006)Google Scholar
  8. 8.
    Malcolm, G., Henderson, J.: Combining top-down processes to guide eye movements during real-world scene search. Journal of Vision 10 (2010)Google Scholar
  9. 9.
    Tatler, B., Vincent, B.: The prominence of behavioural biases in eye guidance. Visual Cognition, 1–26 (2009)Google Scholar
  10. 10.
    Kollmorgen, S., Nortmann, N., Schrder, S., Knig, P.: Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention (2010)Google Scholar
  11. 11.
    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 20, 1254–1259 (1998)CrossRefGoogle Scholar
  12. 12.
    Maioli, C., Benaglio, I., Siri, S., Sosta, K., Cappa, S.: The integration of parallel and serial processing mechanisms in visual search: Evidence from eye movement recording. European Journal of Neuroscience 13, 364–372 (2001)Google Scholar
  13. 13.
    Findlay, J., Gilchrist, I.: Eye guidance and visual search. Eye guidance in reading and scene perception, 295–312 (1998)Google Scholar
  14. 14.
    Levenstein, A.: Binary codes capable of correcting deletions, insertions and reversals. In: Soviet Physics-Doklandy, vol. 10 (1966)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chen Chi
    • 1
  • Laiyun Qing
    • 1
  • Jun Miao
    • 2
  • Xilin Chen
    • 2
  1. 1.Graduate University of Chinese Academy of ScienceBeijingChina
  2. 2.Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina

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