Approximate Translational Building Blocks for Image Decomposition and Synthesis

We introduce approximate translational building blocks for unsupervised
image parsing. 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 co-occurrence patterns. We quantitatively evaluate our method on a large benchmark data set
and obtain clear improvements over state-of-the-art methods. We apply our
method to texture synthesis by integrating building blocks 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.