Webpage Saliency

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


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.


Web Viewing Visual Attention Multiple Kernel Learning 


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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

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