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Contour-based learning for object detection (2005)
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| Venue: | In Proceedings, International Conference on Computer Vision |
| Citations: | 152 - 1 self |
BibTeX
@INPROCEEDINGS{Shotton05contour-basedlearning,
author = {Jamie Shotton and Andrew Blake and Roberto Cipolla},
title = {Contour-based learning for object detection},
booktitle = {In Proceedings, International Conference on Computer Vision},
year = {2005},
pages = {503--510}
}
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Abstract
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudimentary detector is learned from a very small set of segmented images and applied to a larger training set of unsegmented images; the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter. The detectors are learned with a boosting algorithm which creates a location-sensitive classifier using a discriminative set of features from a randomly chosen dictionary of contour fragments. We present results that are very competitive with other state-of-the-art object detection schemes and show robustness to object articulations, clutter, and occlusion. Our major contributions are the application of boosted local contour-based features for object detection in a partially supervised learning framework, and an efficient new boosting procedure for simultaneously selecting features and estimating per-feature parameters. 1.
Keyphrases
object detection contour-based learning rudimentary detector discriminative set learning framework boosted local contour-based feature local contour-based feature unsegmented image small set boosting algorithm major contribution efficient new boosting procedure segmented image per-feature parameter location-sensitive classifier randomly chosen dictionary second stage bootstrap present result training set state-of-the-art object detection scheme learning architecture novel categorical object detection scheme contour fragment
