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Adaboost feature selection

asked 2013-05-01 20:59:00 -0600

I am trying to train an adaboost classifier using the openCV library, for visual pedestrian detection. I've come across the notion that adaboost allows the selection of the most relevant features, meaning, if I harvest 50.000 features from images and then use them to train a classifier, in the end of the training process I would be able to select, for example, the best 2000 out of those 50.000.

Then, this would allow me to harvest only those 2000 during the actual process for the sake of speed.

Is this even true? Or am I falling in a misconception?

If true,, is it possible to be done using the openCV library?

Best regards

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Guanta gravatar imageGuanta ( 2013-05-02 03:39:46 -0600 )edit

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answered 2013-05-02 02:13:19 -0600

Boosting is included in the openCV library on this link. It can indeed be used to specify the best features of a feature set.

I guess the following functionality could be a good place to start:

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Asked: 2013-05-01 20:59:00 -0600

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Last updated: May 02 '13