Systems, methods, and computer program products implementing techniques
for training classifiers. The techniques include receiving a training set
that includes positive images and negative images, receiving a restricted
set of linear operators, and using a boosting process to train a
classifier to discriminate between the positive and negative images. The
boosting process is an iterative process. The iterations include a first
iteration where a classifier is trained by (1) testing some, but not all
linear operators in the restricted set against a weighted version of the
training set, (2) selecting for use by the classifier the linear operator
with the lowest error rate, and (3) generating a re-weighted version of
the training set. The iterations also include subsequent iterations
during which another classifier is trained by repeating steps (1), (2),
and (3), but using in step (1) the re-weighted version of the training
set generated during a previous iteration.