Visual object categorization with new keypoint-based adaBoost featuresReport as inadecuate

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1 CAOR - Centre de Robotique

Abstract : 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.

Author: Taoufik Bdiri - Fabien Moutarde - Bruno Steux -



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