  We present promising results for visual object categorization, obtained with
adaBoost using new original ?keypoints-based features?. These weak-classifiers
produce a boolean response based on presence or absence in the tested image of
a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently
similar (i.e. within a given distance) to a reference descriptor characterizing
the feature. A first experiment was conducted on a public image dataset
containing lateral-viewed cars, yielding 95% recall with 95% precision on test
set. Preliminary tests on a small subset of a pedestrians database also gives
promising 97% recall with 92 % precision, which shows the generality of our new
family of features. Moreover, analysis of the positions of adaBoost-selected
keypoints show that they correspond to a specific part of the object category
(such as ?wheel? or ?side skirt? in the case of lateral-cars) and thus have a
?semantic? meaning. We also made a first test on video for detecting vehicles
from adaBoostselected keypoints filtered in real-time from all detected
keypoints.
