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Quantitative visual attention prediction on webpage images using multiclass SVM

Published: 25 June 2019 Publication History

Abstract

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%).

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  • (2024)Webpage Saliency Prediction Using a Single Layer Support Vector Regressor2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)10.1109/QICAR61538.2024.10496639(80-83)Online publication date: 29-Feb-2024
  • (2023)Predicting Trending Elements on Web Pages Using Machine LearningInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226167740:22(7065-7080)Online publication date: 2-Oct-2023
  • (2022)UsyBus: A Communication Framework among Reusable Agents integrating Eye-Tracking in Interactive ApplicationsProceedings of the ACM on Human-Computer Interaction10.1145/35322076:EICS(1-36)Online publication date: 17-Jun-2022
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  1. Quantitative visual attention prediction on webpage images using multiclass SVM

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    cover image ACM Conferences
    ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications
    June 2019
    623 pages
    ISBN:9781450367097
    DOI:10.1145/3314111
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 June 2019

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    Author Tags

    1. eye-tracking
    2. multi-class classification
    3. support vector machine (SVM)
    4. visual attention
    5. webpage image

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    View all
    • (2024)Webpage Saliency Prediction Using a Single Layer Support Vector Regressor2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)10.1109/QICAR61538.2024.10496639(80-83)Online publication date: 29-Feb-2024
    • (2023)Predicting Trending Elements on Web Pages Using Machine LearningInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226167740:22(7065-7080)Online publication date: 2-Oct-2023
    • (2022)UsyBus: A Communication Framework among Reusable Agents integrating Eye-Tracking in Interactive ApplicationsProceedings of the ACM on Human-Computer Interaction10.1145/35322076:EICS(1-36)Online publication date: 17-Jun-2022
    • (2022)VIS-iTrack: Visual Intention Through Gaze Tracking Using Low-Cost WebcamIEEE Access10.1109/ACCESS.2022.318796910(70779-70792)Online publication date: 2022
    • (2021)A Classification Method for Academic Resources Based on a Graph Attention NetworkFuture Internet10.3390/fi1303006413:3(64)Online publication date: 4-Mar-2021
    • (2021)Eye tracking technology to audit google analyticsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2020.10229459:COnline publication date: 30-Dec-2021
    • (2020)Understanding Visual Saliency in Mobile User Interfaces22nd International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3379503.3403557(1-12)Online publication date: 5-Oct-2020

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