  Superpixels have become very popular in many computer vision applications.
Nevertheless, they remain underexploited since the superpixel decomposition may
produce irregular and non stable segmentation results due to the dependency to
the image content. In this paper, we first introduce a novel structure, a
superpixel-based patch, called SuperPatch. The proposed structure, based on
superpixel neighborhood, leads to a robust descriptor since spatial information
is naturally included. The generalization of the PatchMatch method to
SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a
framework to perform fast segmentation and labeling from an image database, and
demonstrate the potential of our approach since we outperform, in terms of
computational cost and accuracy, the results of state-of-the-art methods on
both face labeling and medical image segmentation.
