This paper is about shape fitting to regions that segment an image and some applications that rely on the abstraction that offers. The novelty lies in three areas: (1) we fit a shape drawn from a selection of shape families, not just one class of shape, using a supervised classifier; (2) We use results from the classifier to match photographs and artwork of particular objects using a few qualitative shapes, which overcomes the significant differences between photographs and paintings; (3) We further use the shape classifier to process photographs into abstract synthetic art which, so far as we know, is novel too. Thus we use our shape classier in both discriminative (matching) and generative (image synthesis) tasks. We conclude the level of abstraction offered by our shape classifier is novel and useful.