@inproceedings{b18d3a1ed344489db7e56056b6db7494,
title = "Webpage saliency",
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 = "Multiple Kernel Learning, Visual Attention, Web Viewing",
author = "Chengyao Shen and Qi Zhao",
year = "2014",
doi = "10.1007/978-3-319-10584-0_3",
language = "English (US)",
isbn = "9783319105833",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 7",
pages = "33--46",
booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",
edition = "PART 7",
note = "13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
}