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Abstract:
The ability to rely on similarity metrics invariant to image
transformations is an important issue for image classification
tasks such as face or character recognition. We analyze an
invariant metric that has performed well for the latter - the
tangent distance - and study its limitations when applied to
regular images, showing that the most significant among these
(convergence to local minima) can be drastically reduced by
computing the distance in a multiresolution setting. This leads to
the multiresolution tangent distance, which exhibits significantly
higher invariance to image transformations, and can be easily
combined with robust estimation procedures.
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