Beyond SVD: Sparse projections onto exemplar orthonormal bases for compact image representation

We present a new method for compact representation of large image datasets. Our method is based on treating small patches from an image as matrices as opposed to the conventional vectorial representation, and encoding those patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques on experiments involving the compression of ORL and Yale face databases.