Advertisement

Webpage Saliency

  • Chengyao Shen
  • Qi Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)

Abstract

Webpage is becoming a more and more important visual input to us. While there are few studies on saliency in webpage, we in this work make a focused study on how humans deploy their attention when viewing webpages and for the first time propose a computational model that is designed to predict webpage saliency. A dataset is built with 149 webpages and eye tracking data from 11 subjects who free-view the webpages. Inspired by the viewing patterns on webpages, multi-scale feature maps that contain object blob representation and text representation are integrated with explicit face maps and positional bias. We propose to use multiple kernel learning (MKL) to achieve a robust integration of various feature maps. Experimental results show that the proposed model outperforms its counterparts in predicting webpage saliency.

Keywords

Web Viewing Visual Attention Multiple Kernel Learning 

References

  1. 1.
    Borji, A.: Boosting bottom-up and top-down visual features for saliency estimation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 438–445. IEEE (2012)Google Scholar
  2. 2.
    Brainard, D.H.: The psychophysics toolbox. Spatial Vision 10(4), 433–436 (1997)CrossRefGoogle Scholar
  3. 3.
    Bruce, N., Tsotsos, J.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3) (2009)Google Scholar
  4. 4.
    Buscher, G., Cutrell, E., Morris, M.R.: What do you see when you’re surfing?: using eye tracking to predict salient regions of web pages. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 21–30. ACM (2009)Google Scholar
  5. 5.
    Cerf, M., Harel, J., Einhäuser, W., Koch, C.: Predicting human gaze using low-level saliency combined with face detection. Advances in Neural Information Processing Systems 20 (2008)Google Scholar
  6. 6.
    Cho, C.H., Cheon, H.J.: Why do people avoid advertising on the internet? Journal of Advertising, 89–97 (2004)Google Scholar
  7. 7.
    Cutrell, E., Guan, Z.: What are you looking for?: an eye-tracking study of information usage in web search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 407–416. ACM (2007)Google Scholar
  8. 8.
    Derrington, A.M., Krauskopf, J., Lennie, P.: Chromatic mechanisms in lateral geniculate nucleus of macaque. The Journal of Physiology 357(1), 241–265 (1984)Google Scholar
  9. 9.
    Einhäuser, W., Spain, M., Perona, P.: Objects predict fixations better than early saliency. Journal of Vision 8(14) (2008)Google Scholar
  10. 10.
    Faraday, P.: Visually critiquing web pages. In: Multimedia’ 89, pp. 155–166. Springer (2000)Google Scholar
  11. 11.
    Garcia-Diaz, A., Leborán, V., Fdez-Vidal, X.R., Pardo, X.M.: On the relationship between optical variability, visual saliency, and eye fixations: A computational approach. Journal of Vision 12(6), 17 (2012)CrossRefGoogle Scholar
  12. 12.
    Grier, R., Kortum, P., Miller, J.: How users view web pages: An exploration of cognitive and perceptual mechanisms. In: Human Computer Interaction Research in Web Design and Evaluation, pp. 22–41 (2007)Google Scholar
  13. 13.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. Advances in Neural Information Processing Systems 19, 545 (2007)Google Scholar
  14. 14.
    Hervet, G., Guérard, K., Tremblay, S., Chtourou, M.S.: Is banner blindness genuine? eye tracking internet text advertising. Applied Cognitive Psychology 25(5), 708–716 (2011)CrossRefGoogle Scholar
  15. 15.
    Hou, X., Harel, J., Koch, C.: Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1), 194–201 (2012)CrossRefGoogle Scholar
  16. 16.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10), 1489–1506 (2000)CrossRefGoogle Scholar
  17. 17.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  18. 18.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113. IEEE (2009)Google Scholar
  19. 19.
    Nielsen, J.: F-shaped pattern for reading web content (2006)Google Scholar
  20. 20.
    Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: Simplemkl. Journal of Machine Learning Research 9(11) (2008)Google Scholar
  21. 21.
    Still, J.D., Masciocchi, C.M.: A saliency model predicts fixations in web interfaces. In: 5 th International Workshop on Model Driven Development of Advanced User Interfaces (MDDAUI 2010), p. 25 (2010)Google Scholar
  22. 22.
    Stone, B., Dennis, S.: Using lsa semantic fields to predict eye movement on web pages. In: Proc. 29th Cognitive Science Society Conference, pp. 665–670 (2007)Google Scholar
  23. 23.
    Stone, B., Dennis, S.: Semantic models and corpora choice when using semantic fields to predict eye movement on web pages. International Journal of Human-Computer Studies 69(11), 720–740 (2011)CrossRefGoogle Scholar
  24. 24.
    Tatler, B.W.: The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision 7(14), 4 (2007)CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I–511. IEEE (2001)Google Scholar
  27. 27.
    Zhang, L., Tong, M., Marks, T., Shan, H., Cottrell, G.: Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8(7) (2008)Google Scholar
  28. 28.
    Zhao, Q., Koch, C.: Learning a saliency map using fixated locations in natural scenes. Journal of Vision 11(3) (2011)Google Scholar
  29. 29.
    Zhao, Q., Koch, C.: Learning visual saliency. In: 2011 45th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chengyao Shen
    • 1
    • 2
  • Qi Zhao
    • 2
  1. 1.Graduate School for Integrated Science and EngineeringNational University of SingaporeSingapore
  2. 2.Department of Electrical and Computer EngineeringNational University of SingaporeSingapore

Personalised recommendations