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Concept of Cascade Classifier

asked 2016-09-01 12:16:53 -0500

zvone gravatar image

I am trying to undrestand conceptually how does Cascade Classifier training works in OpenCV using LBP. I understand that AdaBoost is used for choosing weak classifiers and combining them to make a strong classifier. I also understand that LBP is used as visual descriotor of features. But the thing I could not find out is how are weak classifiers actually created from LBP? Is there any learning algorithm used, like Support vector machine? Thanks for help.

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answered 2016-09-02 03:23:50 -0500

Well, each LBP feature that was selected during training is used to create binary decision trees. In the default case, these are stumps (1 layer decisions) with a decision weight on the score calculated by the LBP feature. Combining several stumps/decision trees lead to a weak stage.

This is described in full detail in OpenCV 3 Blueprints, Chapter 5!

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Asked: 2016-09-01 12:16:53 -0500

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Last updated: Sep 02 '16