Work

Edit Distance between Merge Trees

Year: 2018

Type: article

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.

Source: IEEE Transactions on Visualization and Computer Graphics

DOI: 10.1109/tvcg.2018.2873612

Authors: Raghavendra Sridharamurthy, Talha Bin Masood, Adhitya Kamakshidasan, Vijay Natarajan

Institution: Indian Institute of Science Bangalore

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Indian Institute of Science

Award: G5384174897