Ask Your Question

Adaboost feature selection

asked 2013-05-01 20:59:00 -0500

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

edit retag flag offensive close merge delete


Note: Please try the search first, a similar question has already been asked:

Guanta gravatar imageGuanta ( 2013-05-02 03:39:46 -0500 )edit

1 answer

Sort by ยป oldest newest most voted

answered 2013-05-02 02:13:19 -0500

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:

edit flag offensive delete link more

Question Tools


Asked: 2013-05-01 20:59:00 -0500

Seen: 855 times

Last updated: May 02 '13