Abstract: We introduce approximate translational building blocks for unsupervised image decomposition. Such building blocks are frequently appearing copies of image patches that are mapped coherently under translations. We exploit the coherency assumption to find approximate building blocks in noisy and ambiguous image data, using a spectral embedding of re-occurrence patterns. We quantitatively evaluate our method on a large benchmark dataset and obtain clear improvements over state-of-the-art methods. We apply our method to texture synthesis by integrating building block constraints and their offset statistics into a conventional Markov random field model. A user study shows improved retargeting results even if the images are only partially described by a few classes of building blocks.