Contour-Based Learning for Object Detection

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Contour-Based Learning for Object Detection
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 s...
Jamie Shotton, Andrew Blake, Roberto Cipolla
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICCV
Authors Jamie Shotton, Andrew Blake, Roberto Cipolla
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