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Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Max Planck Institute for Informatics. (Visualization and Data Analysis)
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Max Planck Institute for Informatics. (Visualization and Data Analysis)ORCID iD: 0000-0002-1498-9062
2014 (English)In: Computer Graphics Forum (Proc. EuroVis), ISSN 1467-8659, Vol. 33, no 3, 41-50 p.Article in journal (Refereed) PublishedText
Abstract [en]

We present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other - in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2014. Vol. 33, no 3, 41-50 p.
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
URN: urn:nbn:se:kth:diva-184831DOI: 10.1111/cgf.12360ISI: 000340597400005ScopusID: 2-s2.0-84904414532OAI: oai:DiVA.org:kth-184831DiVA: diva2:916911
Note

QC 20160406

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-04-06Bibliographically approved

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