 |
    |
Webpage Saliency
Chengyao Shen1, 2 and Qi Zhao2
1Graduate School for Integrated Science and Engineering, National University of Singapore, Singapore
2Department of Electrical and Computer Engineering, National University of Singapore, Singapore
eleqiz@nus.edu.sg
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
LNCS 8695, p. 33 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014