Experiments with an Extended Tangent Distance
Daniel Keysers, Jörg Dahmen, Thomas Theiner, Hermann Ney
Lehrstuhl für Informatik VI
RWTH Aachen - University of Technology,
52056 Aachen, Germany
keysers, dahmen, theiner, firstname.lastname@example.org
Invariance is an important aspect in image object recognition.
We present results obtained with an extended tangent distance
incorporated in a kernel density based Bayesian
classifier to compensate for affine image variations. An image
for local variations is
introduced and its relationship to
tangent distance is considered. The proposed classification
algorithms are evaluated on databases of different domains.
An excellent result of 2.2%
error rate on the original USPS handwritten digits recognition task
is obtained. On a database of radiographs from daily routine, best results are obtained
by combining tangent distance and the proposed distortion model.