Abstract: 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.