Fixation-indices based correlation between text and image visual features of webpages

Abstract

Web elements associate with a set of visual features based on their data modality. For example, text associated with font-size and font-family whereas images associate with intensity and color. The unavailability of methods to relate these heterogeneous visual features limiting the attention-based analyses on webpages. In this paper, we propose a novel approach to establish the correlation between text and image visual features that influence users' attention. We pair the visual features of text and images based on their associated fixation-indices obtained from eye-tracking. From paired data, a common subspace is learned using Canonical Correlation Analysis (CCA) to maximize the correlation between them. The performance of the proposed approach is analyzed through a controlled eye-tracking experiment conducted on 51 real-world webpages. A very high correlation of 99.48% is achieved between text and images with text related font families and image related color features influencing the correlation.