We present a contour based approach to object recognition in real-world images. Contours are represented by generic shape primitives of line segments and ellipses. These primitives offer substantial flexibility to model complex shapes. We pair connected primitives as shape tokens, and learn category specific combinations of shape tokens. We do not restrict combinations to have a fixed number of tokens, but allow each combination to flexibly evolve to best represent a category. This, coupled with the generic nature of primitives, enables a variety of discriminative shape structures of a category to be learned. We compare our approach with related methods and state-of-the-art contour based approaches on two demanding datasets across 17 categories. Highly competitive results are obtained. In particular, on the challenging Weizmann horse dataset, we attain improved image classification and object detection results over the best contour based results published so far.