In the new C++ interface it is also possible to use LBP (local binary pattern) features in addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR,2001. The paper is available online athttps://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf（上述有提到）

7、opencv关于boost

(boost.cpp——opencv3.0)

Boosting

A common machine learning task is supervised learning. In supervised learning,the goal is to learn the functional relationship F:y=F(x) between the input x and the output y . Predicting the qualitative output is calledclassification,while predicting the quantitative output is calledregression.

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[125]. 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.

Decision trees are the most popular weak classifiers used in boosting schemes. Often the simplest decision trees with only a single split node per tree (called stumps ) are sufficient.

The boosted model is based on N training examples (xi,yi)1N with xi∈RK and yi∈−1,+1 . xi is a K -component vector. Each component encodes a feature relevant to the learning task at hand. The desired two-class output is encoded as -1 and +1.

Different variants of boosting are known as Discrete Adaboost,Real AdaBoost,LogitBoost,and Gentle AdaBoost[49]. All of them are very similar in their overall structure. Therefore,this chapter focuses only on the standard two-class Discrete AdaBoost algorithm,outlined below. Initially the same weight is assigned to each sample (step 2). Then,a weak classifier fm(x) is trained on the weighted training data (step 3a). Its weighted training error and scaling factor cm is computed (step 3b). The weights are increased for training samples that have been misclassified (step 3c). All weights are then normalized,and the process of finding the next weak classifier continues for another M -1 times. The final classifier F(x) is the sign of the weighted sum over the individual weak classifiers (step 4).

Two-class Discrete AdaBoost Algorithm

• Set N examples (xi,yi)1N with xi∈RK,yi∈−1,+@H_475_1403@1 .
• Assign weights as wi=1/N,i=1,...,N .
• Repeat for m=1,2,...,M :
  • Fit the classifier @H_151_1502@fm(x)∈−1,1 ,using weights wi on the training data.
  • Compute errm=Ew[1(y≠fm(x))],cm=log((1−errm)/errm) .
  • Set wi⇐wiexp[cm1(yi@H_403_1714@≠fm(xi))],i=1,2,...,N, and renormalize so that Σiwi=1 .
• Classify new samplesxusing the formula: sign(Σm=1Mcmfm(x)) .

Note

Similar to the classical boosting methods,the current implementation supports two-class classifiers only. For M > 2 classes,there is the AdaBoost.MHalgorithm (described in [49]) that reduces the problem to the two-class problem,yet with a much larger training set.

To reduce computation time for boosted models without substantially losing accuracy,the influence trimming technique can be employed. As the training algorithm proceeds and the number of trees in the ensemble is increased,a larger number of the training samples are classified correctly and with increasing confidence,thereby those samples receive smaller weights on the subsequent iterations. Examples with a very low relative weight have a small impact on the weak classifier training. Thus,such examples may be excluded during the weak classifier training without having much effect on the induced classifier. This process is controlled with the weight_trim_rate parameter. Only examples with the summary fraction weight_trim_rate of the total weight mass are used in the weak classifier training. Note that the weights foralltraining examples are recomputed at each training iteration. Examples deleted at a particular iteration may be used again for learning some of the weak classifiers further[49]

See also

cv::ml::Boost

Prediction with Boost

StatModel::predict(samples,results,flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get the raw sum from Boost classifier.

8、关于训练过程打印信息的解释

1）POS count : consumed n1 : n2

每次都调用updateTrainingSet( requiredLeafFARate,tempLeafFARate );函数