Our supervised streamline segmentation algorithm allows the extraction of user-defined flow features. For each data set, users are required to manually segment only a small number of streamlines in order to define what flow features they want to extract from the flow field. The user-picked segmentation points along a streamline will be used to generate positive training examples, whereas the remaining ones are used to generate negative training examples. Multi-scale feature vectors are computed for each positive and negative example, and fed into a binary support vector machine (SVM) trainer. Finally, we use the trained classifier to determine the segmentation points for all the streamlines in the data set. A post-processing step is required for grouping nearby segmentation points detected by the classifier.
Yifei Li, Chaoli Wang, and Ching-Kuang Shene. Extracting Flow Features via Supervised Streamline Segmentation. Computers & Graphics, 52:79-92, Nov 2015. [PDF]