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Edit Distance between Merge Trees.

Raghavendra Sridharamurthy, Talha Bin Masood, Adhitya Kamakshidasan

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    Summary
    This summary is machine-generated.

    We developed a tree edit distance method to compare topological merge trees. This approach efficiently measures similarity for feature-directed analysis of complex scalar fields.

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    Area of Science:

    • Data Visualization
    • Computational Geometry
    • Scientific Computing

    Background:

    • Topological structures like merge trees offer concise representations of scalar fields.
    • Merge trees are crucial for visualizing and exploring complex, feature-rich datasets.
    • Comparing merge trees is vital for analyzing time-varying data and identifying features.

    Purpose of the Study:

    • To introduce an efficient and metric-compliant method for comparing merge trees.
    • To enable feature-directed analysis of scalar fields using topological similarity.
    • To provide an intuitive cost model for edit operations in merge tree comparison.

    Main Methods:

    • Utilizing tree edit distance to quantify the similarity between merge trees.
    • Developing a cost model that reflects intrinsic properties of merge trees.
    • Implementing and evaluating the approach on diverse datasets.

    Main Results:

    • The proposed tree edit distance method is efficient and satisfies metric properties.
    • Experimental results demonstrate its utility across various data types, including time-varying fields and cryo-electron microscopy data.
    • The method facilitates effective feature-driven analysis of scalar fields.

    Conclusions:

    • Tree edit distance offers a robust and efficient solution for merge tree comparison.
    • This approach enhances the feature-directed analysis of scalar fields.
    • The method is broadly applicable to scientific visualization and data analysis challenges.