Visual object categorization with new keypoint-based adaBoost features

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 adaBoost-selected keypoints filtered in real-time from all detected keypoints.