Extracting flow features via supervised streamline segmentation
Department of Computer Science
Effective flow feature extraction enables users to explore complex flow fields by reducing visual clutter. Existing methods usually use streamline segmentation as a preprocessing step for feature extraction. In our work, features are directly extracted as a result of streamline segmentation. In order to achieve this, we first ask users to specify desired features by manually segmenting a few streamlines from a flow field. Users only need to pick the segmentation points (i.e., positive examples) along a streamline, remaining points will be used as negative examples. Next we compute multiscale features for each positive/negative example and feed them into a binary support vector machine (SVM) trainer. The trained classifier is then used to segment all the streamlines in a flow field. Finally, the segments are clustered based on their shape similarities. Our experiment shows that very good segmentation results can be obtained with only a small number of streamlines to be segmented by users for each data set. We also propose a novel heuristic based on the minimum bounding ellipsoid volume to help determine where to segment a streamline.
Computers & Graphics
Extracting flow features via supervised streamline segmentation.
Computers & Graphics,
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