Webpage saliency prediction with multi-features fusion
Jian Li, Li Fen Su, Bo Wu, Junbiao Pang, Chunfeng Wang, Zhe Wu, Qingming Huang · 2016
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features according to our observation on webpages characters and related eye-tracking data. Further, in order to achieve a flexible adaptation to various types of webpages, we employed a machine-learning framework based on our proposed features. Experimental results demonstrate that our model outperforms other state-of-the-art methods in webpage saliency prediction.