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Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics.

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    This study introduces an automated infographic generation method that imitates online examples. The retrieve-then-adapt approach uses visual elements for retrieval and recursive neural networks for adaptation, simplifying infographic creation.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Infographic creation is complex and time-consuming, posing challenges for users with limited design expertise.
    • Existing automated methods rely on predefined blueprints, which are limited in diversity and volume.
    • Abundant online infographics offer diverse design styles and serve as valuable examples.

    Purpose of the Study:

    • To develop an automated approach for generating infographics by imitating existing online examples.
    • To address the limitations of predefined blueprints in automated infographic generation.

    Main Methods:

    • A two-stage retrieve-then-adapt approach is proposed.
    • The retrieval stage indexes online infographics by visual elements and queries them using user information.
    • The adaptation stage refines initial drafts using a Markov Chain Monte Carlo (MCMC)-like method and recursive neural networks.

    Main Results:

    • The system generates infographics by retrieving and adapting existing examples.
    • The adaptation stage iteratively improves visual appearance to fit user information.
    • Effectiveness demonstrated on proportion-related infographics with sample results and expert reviews.

    Conclusions:

    • The proposed retrieve-then-adapt method effectively automates infographic generation by learning from online examples.
    • This approach offers a more diverse and adaptable solution compared to blueprint-based methods.
    • The integration of MCMC-like techniques and recursive neural networks enhances the visual quality and user-data fit of generated infographics.