LBP train_cascade process of training

asked 2016-09-05 17:10:54 -0600

zvone
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I am trying to understand what is train_cascade ( using LBP) actually doing with samples. If I have, for example, 1000 positives and 1000 negatives, it calculates LBP histogram for each, right? And what does it do with all of these histograms? From which one does it choose features to make weak learners?

answered 2016-09-09 06:44:54 -0600

StevenPuttemans

20029 ●16 ●82 ●207 http://stevenputtemans...

Ow wait, you are mixing two things up.

On the one hand you have LBP histograms, used for recognition for example.
On the other hand you have LBP features, that are fed to AdaBoost, a boosting process to create a cascade of weak classifiers (binary stump trees by default)

If you want a step by step explanation of the process, I suggest you to grab Chapter 5 of OpenCV 3 Blueprints! It goes through the process step by step.

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I got the book. Thanks for recommendation. Still don't understand from which of these 1000 positives are LBP features extracted from?

zvone ( 2016-09-10 05:57:53 -0600 )edit

during training all possible LBP features from each training sample are calculated. Since they all have the same dimensions, or are rescaled to that,they have the exact same feature locations. This is enforced by a feature grid. Then the boosting decides which of these LBP grid features are descrimintatitve enough to seperate the negatives from the positives and makes a model out of them. Now at detection time, only those features selected are calculated on each given sample. Does that clarify it further?

StevenPuttemans ( 2016-09-12 03:53:47 -0600 )edit

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