Multiresolution Tangent Distance for Affine-invariant Classification

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Nuno Vasconcelos, Andrew Lippman


The ability to rely on similarity metrics invariant to image transforma(cid:173) tions 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 multi resolution tangent distance, which exhibits significantly higher invariance to im(cid:173) age transformations, and can be easily combined with robust estimation procedures.