This paper proposes a new approach for finding expressive and geometrically invariant parts for modeling 3D objects. The approach relies on identifying groups of local affine regions (image features having a characteristic appearance and elliptical shape) that remain approximately affinely rigid across a range of views of an object, and across multiple instances of the same object class. These groups, termed semi-local affine parts, are learned using correspondence search between pairs of unsegmented and cluttered input images, followed by validation against additional training images. The proposed approach is applied to the recognition of butterflies in natural imagery.