Investigating and Modeling the Web Elements’ Visual Feature Influence on Free-viewing Attention
| Autor: | Vijaya Saradhi Vedula, Samit Bhattacharya, Sandeep Vidyapu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Brightness
Color histogram Computer Networks and Communications Computer science business.industry Pattern recognition 02 engineering and technology Rendering (computer graphics) Feature (computer vision) 020204 information systems Font Web page 0202 electrical engineering electronic engineering information engineering Eye tracking Visual attention 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ACM Transactions on the Web. 15:1-27 |
| ISSN: | 1559-114X 1559-1131 |
| DOI: | 10.1145/3409474 |
| Popis: | User attentional analyses on web elements help in synthesis and rendering of webpages. However, majority of the existing analyses are limited in incorporating the intrinsic visual features of text and images. This study aimed to analyze the influence of elements’ visual features (font-size, font-family, color, etc., for text; and brightness, color, intensity, etc., for images) besides their position on users’ free-viewing visual attention. The investigation includes: (i) user’s position-based attention allocation on text and image web elements, (ii) identification of informative visual features with respect to the attention, (iii) performance of informative visual features in predicting the ordinal visual attention (fixation-indices). Towards the study, an eye-tracking experiment was conducted with 42 participants on 36 real-world webpages. The analyses revealed: (i) Though users predominantly allocate the initial attention to MiddleCenter}, MiddleLeft, TopCenter, TopLeft regions, the elements in Right and Bottom regions are not completely ignored; (ii) Space -related (column-gap, line-height, padding) and font Size -related (font-size, font-weight) intrinsic text features, and Mid-level Color Histogram intrinsic image features are informative, while position and size are informative for both the types; (iii) the informative visual features predict the ordinal visual attention on an element with 90% average accuracy and 70% micro-F1 score. Our approach finds applications in element-granular web-designing and user attention prediction. |
| Databáze: | OpenAIRE |
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