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PAMI
2007

Sharing Visual Features for Multiclass and Multiview Object Detection

13 years 2 months ago
Sharing Visual Features for Multiclass and Multiview Object Detection
We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. We present a multi-task learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the run-time cost of the classifier, is observed to scale app...
Antonio Torralba, Kevin P. Murphy, William T. Free
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PAMI
Authors Antonio Torralba, Kevin P. Murphy, William T. Freeman
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