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Fast Similarity Search in Scalar Fields using Merging Histograms
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Visualization and Data Analysis)
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Visualization and Data Analysis)ORCID iD: 0000-0002-1498-9062
2015 (English)In: TopoInVis, 2015, 1-14 p.Conference paper (Refereed)Text
Abstract [en]

Similarity estimation in scalar fields using level set topology has attracted a lot of attention in the recent past. Most existing techniques match parts of contour or merge trees against each other by estimating a best overlap between them. Due to their combinatorial nature, these methods can be computationally expensive or prone to instabilities. In this paper, we use an inexpensive feature descriptor to compare subtrees of merge trees against each other. It is the data histogram of the voxels encompassed by a subtree. A small modification of the merge tree computation algorithm allows for obtaining these histograms very efficiently. Furthermore, the descriptor is robust against instabilities in the merge tree. The method is useful in an interactive environment, where a user can search for all structures similar to an interactively selected one. Our method is conservative in the sense that it finds all similar structures, with the rare occurrence of some false positives. We show with several examples the effectiveness, efficiency and accuracy of our method.

Place, publisher, year, edition, pages
2015. 1-14 p.
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
URN: urn:nbn:se:kth:diva-184849OAI: oai:DiVA.org:kth-184849DiVA: diva2:916936
Conference
TopoInVis 2015, May 20-22
Note

QC 20160406

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

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Fast Similarity Search in Scalar Fields using Merging Histograms

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Saikia, HimangshuWeinkauf, Tino
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