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Proceedings Paper

Normalized-cut algorithm for hierarchical vector field data segmentation
Author(s): Jiann-Liang Chen; Zhaojun Bai; Bernd Hamann; Terry J. Ligocki
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Paper Abstract

In the context of vector field data visualization, it is often desirable to construct a hierarchical data representation. One possibility to construct a hierarchy is based on clustering vectors using certain similarity criteria. We combine two fundamental approaches to cluster vectors and construct hierarchical vector field representations. For clustering, a locally constructed linear least-squares approximation is incorporated into a similarity measure that considers both Euclidean distance between point pairs (for which dependent vector data are given) and difference in vector values. A modified normalized cut (NC) method is used to obtain a near-optimal clustering of a given discrete vector field data set. To obtain a hierarchical representation, the NC method is applied recursively after the construction of coarse-level clusters. We have applied our NC-based segmentation method to simple, analytically defined vector fields as well as discrete vector field data generated by turbulent flow simulation. Our test results indicate that our proposed adaptation of the original NC method is a promising method as it leads to segmentation results that capture the qualitative and topological nature of vector field data.

Paper Details

Date Published: 9 June 2003
PDF: 12 pages
Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); doi: 10.1117/12.477521
Show Author Affiliations
Jiann-Liang Chen, Univ. of California/Davis (United States)
Zhaojun Bai, Univ. of California/Davis (United States)
Bernd Hamann, Univ. of California/Davis (United States)
Lawrence Berkeley National Lab. (United States)
Terry J. Ligocki, Lawrence Berkeley National Lab. (United States)

Published in SPIE Proceedings Vol. 5009:
Visualization and Data Analysis 2003
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Groehn; Katy Boerner, Editor(s)

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