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Inspired by the abstracting, focusing and explanatory qualities of diagram drawing in art, in this paper we propose a novel seeding strategy to generate representative and illustrative streamlines in 2D vector fields to enforce visual clarity and evidence. A particular focus of our algorithm is to depict the underlying flow patterns effectively and succinctly with a minimum set of streamlines. To achieve this goal, 2D distance fields are generated to encode the distances from each grid point in the field to the nearby streamlines. A local metric is derived to measure the dissimilarity between the vectors from the original field and an approximate field computed from the distance fields. A global metric is used to measure the dissimilarity between streamlines based on the local errors to decide whether to drop a new seed at a local point. This process is iterated to generate streamlines until no more streamlines can be found that are dissimilar to the existing ones. We present examples of images generated from our algorithm and report results from qualitative analysis and user studies.