Hello,
I am familar with the opencv_createsamples and opencv_traincascade tool. During my reading, i came about to read this documentation https://docs.opencv.org/3.1.0/dc/dd6/ml_intro.html
"Boosting is a powerful learning concept that provides a solution to the supervised classification learning task. It combines the performance of many "weak" classifiers to produce a powerful committee [139] . A weak classifier is only required to be better than chance, and thus can be very simple and computationally inexpensive. However, many of them smartly combine results to a strong classifier that often outperforms most "monolithic" strong classifiers such as SVMs and Neural Networks."
So how i understand it i could train several small feature classifiers and use them together. For example train a face and body classifier if want to detect humans.
Is my understandig correct / does this makes sense? And if so - can i do it from command line or do i have to do it programatically?
Thanks four your answer and sorry for the maybe newbie question.