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Graph-Based Information Block Detection in Infographic With Gestalt Organization Principles.

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    This study introduces a new graph-based model for detecting information blocks in infographics. By incorporating chromatic and structural features alongside spatial proximity, the model improves detection accuracy for infographic visualization charts.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Infographics utilize information blocks for data presentation.
    • Current detection methods primarily rely on spatial proximity, often missing crucial elements.
    • Ignoring chromatic and structural features leads to inaccuracies in information block detection.

    Purpose of the Study:

    • To propose a novel graph-based model for enhanced information block detection in infographics.
    • To address limitations of existing methods by integrating Gestalt Organization Principles.
    • To improve the accuracy and completeness of infographic element grouping.

    Main Methods:

    • Representing infographics using a scene graph structure.
    • Developing a graph-based detection model incorporating spatial proximity, chromatic similarity, and structural similarity.
    • Constructing a new dataset specifically for information block detection in infographics.

    Main Results:

    • The proposed graph-based model significantly outperforms traditional spatial proximity-based methods.
    • Quantitative and qualitative experiments validate the model's effectiveness in detecting information blocks.
    • The model demonstrates superior performance in grouping infographic elements accurately.

    Conclusions:

    • The graph-based approach effectively leverages Gestalt principles for robust information block detection.
    • This method offers a more comprehensive solution for analyzing and understanding infographic structures.
    • The developed dataset and model advance the field of infographic information extraction.