Normalized-cut algorithm for hierarchical vector field data segmentation
Normalized-cut algorithm for hierarchical vector field data segmentation
复制标题
分层矢量场数据分割的归一化切割算法
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
T. Ligocki
中科院分区：
文献类型：
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作者：
Jiann;Z. Bai;B. Hamann;T. Ligocki
在矢量场数据可视化的背景下，通常期望构造分层数据表示。构建层次结构的一种可能性是基于使用某些相似性标准的聚类向量。我们结合联合收割机两个基本的方法来集群向量和构建层次向量场表示。对于聚类，一个本地构造的线性最小二乘近似被纳入一个相似性度量，考虑两个点对之间的欧氏距离（相关的矢量数据）和矢量值的差异。提出了一种改进的归一化割（NC）方法，用于对给定的离散矢量场数据集进行近优聚类。为了获得一个层次化的表示，NC方法递归地应用后，粗级集群的建设。我们已经将我们的基于NC的分割方法应用于简单的、解析定义的矢量场以及由湍流模拟生成的离散矢量场数据。我们的测试结果表明，我们提出的适应原来的NC方法是一个很有前途的方法，因为它会导致分割结果，捕捉矢量场数据的定性和拓扑性质。
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.