Edit Distance between Merge Trees
IEEE Transactions in Visualization and Computer Graphics, 2020
Abstract:
Topological structures such as the merge tree provide an abstract and succinct representation of scalar fields. They facilitate effective visualization and interactive exploration of feature-rich data. A merge tree captures the topology of sub-level and super-level sets in a scalar field. Estimating the similarity between merge trees is an important problem with applications to feature-directed visualization of time-varying data. We present an approach based on tree edit distance to compare merge trees. The comparison measure satisfies metric properties, it can be computed efficiently, and the cost model for the edit operations is both intuitive and captures well-known properties of merge trees. Experimental results on time-varying scalar fields, 3D cryo electron microscopy data, shape data, and various synthetic datasets show the utility of the edit distance towards a feature-driven analysis of scalar fields.
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To cite: Raghavendra Sridharamurthy, Talha Bin Masood, Adhitya Kamakshidasan and Vijay Natarajan "Edit Distance between Merge Trees" IEEE Transactions in Visualization and Computer Graphics, 2020 10.1109/TVCG.2018.2873612
BibTeX:
@article{SridharamurthyetalTVCG2020, author = {Raghavendra Sridharamurthy and Talha Bin Masood and Adhitya Kamakshidasan and Vijay Natarajan}, journal = {{IEEE Transactions on Visualization and Computer Graphics}}, number = {3}, pages = {1518-1531}, title = {Edit Distance between Merge Trees}, volume = {26}, year = {2020} }