Visual object categorization with new keypoint-based adaBoost features

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.
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  • HAL Id : hal-00422580, version 1
  • ARXIV : 0910.1294

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Taoufik Bdiri, Fabien Moutarde, Bruno Steux. Visual object categorization with new keypoint-based adaBoost features. IEEE Symposium on Intelligent Vehicles (IV'2009), Jun 2009, XiAn, China. ⟨hal-00422580⟩

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