Quantitative visual attention prediction on webpage images using multiclass SVM

Webpage images---image elements on a webpage---are prominent to draw user attention. Modeling attention on webpage images helps in their synthesis and rendering. This paper presents a visual feature-based attention prediction model for webpage images. Firstly, fixated images were assigned quantitative visual attention based on users' sequential attention allocation on webpages. Subsequently, fixated images' intrinsic visual features were extracted along with position and size on respective webpages. A multiclass support vector machine (multiclass SVM) was learned using the visual features and associated attention. In tandem, a majority-voting-scheme was employed to predict the quantitative visual attention for test webpage images. The proposed approach was analyzed through an eye-tracking experiment conducted on 36 real-world webpages with 42 participants. Our model outperforms (average accuracy of 91.64% and micro F1-score of 79.1%) the existing position and size constrained regression model (average accuracy of 73.92% and micro F1-score of 34.80%).