Advertisement

Algorithm for Discriminating Aggregate Gaze Points: Comparison with Salient Regions-Of-Interest

  • Thomas J. Grindinger
  • Vidya N. Murali
  • Stephen Tetreault
  • Andrew T. Duchowski
  • Stan T. Birchfield
  • Pilar Orero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

A novel method for distinguishing classes of viewers from their aggregated eye movements is described. The probabilistic framework accumulates uniformly sampled gaze as Gaussian point spread functions (heatmaps), and measures the distance of unclassified scanpaths to a previously classified set (or sets). A similarity measure is then computed over the scanpath durations. The approach is used to compare human observers’s gaze over video to regions of interest (ROIs) automatically predicted by a computational saliency model. Results show consistent discrimination between human and artificial ROIs, regardless of either of two differing instructions given to human observers (free or tasked viewing).

Keywords

Video Sequence Video Frame Human Observer Saliency Model Dynamic Medium 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Land, M.F., Tatler, B.W.: Looking and Acting: Vision and Eye Movements in Natural Behavior. Oxford University Press, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Smith, T.J., Henderson, J.M.: Edit Blindness: The Relationship Between Attention and Global Change Blindness in Dynamic Scenes. Journal of Eye Movement Research 2, 1–17 (2008)Google Scholar
  3. 3.
    Franchak, J.M., Kretch, K.S., Soska, K.C., Babcock, J.S., Adolph, K.E.: Head-Mounted Eye-Tracking of Infants’ Natural Interactions: A New Method. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 21–27. ACM, New York (2010)Google Scholar
  4. 4.
    d’Ydewalle, G., Desmet, G., Van Rensbergen, J.: Film perception: The processing of film cuts. In: Underwood, G.D.M. (ed.) Eye guidance in reading and scene perception, pp. 357–367. Elsevier Science Ltd., Oxford (1998)CrossRefGoogle Scholar
  5. 5.
    Grindinger, T., Duchowski, A.T., Sawyer, M.: Group-Wise Similarity and Classification of Aggregate Scanpaths. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 101–104. ACM, New York (2010)Google Scholar
  6. 6.
    Yarbus, A.L.: Eye Movements and Vision. Plenum Press, New York (1967)CrossRefGoogle Scholar
  7. 7.
    Privitera, C.M., Stark, L.W.: Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22, 970–982 (2000)CrossRefGoogle Scholar
  8. 8.
    Jarodzka, H., Holmqvist, K., Nyström, M.: A Vector-Based, Multidimensional Scanpath Similarity Measure. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 211–218. ACM, New York (2010)Google Scholar
  9. 9.
    Duchowski, A.T., Driver, J., Jolaoso, S., Ramey, B.N., Tan, W., Robbins, A.: Scanpath Comparison Revisited. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pp. 219–226. ACM, New York (2010)Google Scholar
  10. 10.
    Pomplun, M., Ritter, H., Velichkovsky, B.: Disambiguating Complex Visual Information: Towards Communication of Personal Views of a Scene. Perception 25, 931–948 (1996)CrossRefGoogle Scholar
  11. 11.
    Wooding, D.S.: Fixation Maps: Quantifying Eye-Movement Traces. In: ETRA 2002: Proceedings of the 2002 Symposium on Eye Tracking Research & Applications, pp. 31–36. ACM, New York (2002)Google Scholar
  12. 12.
    Hembrooke, H., Feusner, M., Gay, G.: Averaging Scan Patterns and What They Can Tell Us. In: ETRA 2006: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications, p. 41. ACM, New York (2006)Google Scholar
  13. 13.
    Dempere-Marco, L., Hu, X.P., Ellis, S.M., Hansell, D.M., Yang, G.Z.: Analysis of Visual Search Patterns With EMD Metric in Normalized Anatomical Space. IEEE Transactions on Medical Imaging 25, 1011–1021 (2006)CrossRefGoogle Scholar
  14. 14.
    Torstling, A.: The Mean Gaze Path: Information Reduction and Non-Intrusive Attention Detection for Eye Tracking. Master’s thesis, The Royal Institute of Technology, Stockholm, Sweden, Techreport XR-EE-SB 2007:008 (2007)Google Scholar
  15. 15.
    Airola, A., Pahikkala, T., Waegeman, W., De Baets, B., Salakoski, T.: A Comparison of AUC Estimators in Small-Sample Studies. In: Proceedings of the 3rd International workshop on Machine Learning in Systems Biology, pp. 15–23 (2009)Google Scholar
  16. 16.
    Paris, S., Durand, F.: A Fast Approximation of the Bilateral Filter using a Signal Processing Approach. Technical Report MIT-CSAIL-TR-2006-073, Massachusetts Institute of Technology (2006)Google Scholar
  17. 17.
    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 (PAMI) 20, 1254–1259 (1998)CrossRefGoogle Scholar
  18. 18.
    Leigh, R.J., Zee, D.S.: The Neurology of Eye Movements, 2nd edn. Contemporary Neurology Series. F. A. Davis Company, Philadelphia (1991)Google Scholar
  19. 19.
    Grindinger, T.J.: Event-Driven Similarity and Classification of Scanpaths. PhD thesis, Clemson University, Clemson, SC (2010)Google Scholar
  20. 20.
    Peters, R.J., Itti, L.: Computational Mechanisms for Gaze Direction in Interactive Visual Environments. In: ETRA 2006: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications, pp. 27–32. ACM, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas J. Grindinger
    • 1
  • Vidya N. Murali
    • 1
  • Stephen Tetreault
    • 2
  • Andrew T. Duchowski
    • 1
  • Stan T. Birchfield
    • 1
  • Pilar Orero
    • 3
  1. 1.Clemson UniversityUSA
  2. 2.Rhode Island CollegeUSA
  3. 3.Universitat Autònoma de BarcelonaSpain

Personalised recommendations