一,简介
该模块为opencv的机器学习(machine learning,ml)代码库,包含各种机器学习算法:
0, class CvStatModel ; class CvMLData; struct CvParamGrid;
1,bayesian,Normal Bayes Classifier(贝叶斯分类);
2,K-Nearest Neighbour Classifier(K-邻近算法);
3,SVM,support vector machine(支持向量机);
4,Expectation - Maximization (EM算法);
5,Decision Tree(决策树);
6,Random Trees Classifier(随机森林算法);
7,Extremely randomized trees Classifier(绝对随机森林算法);
8, Boosted tree classifier (Boost树算法);
9,Gradient Boosted Trees (梯度Boost树算法);
10,ANN,Artificial Neural Networks(人工神经网络);
二,分析
namespace cv { typedef CvStatModel StatModel; typedef CvParamGrid ParamGrid; typedef CvNormalBayesClassifier NormalBayesClassifier; typedef CvKNearest KNearest; typedef CvSVMParams SVMParams; typedef CvSVMKernel SVMKernel; typedef CvSVMSolver SVMSolver; typedef CvSVM SVM; typedef CvDTreeParams DTreeParams; typedef CvMLData TrainData; typedef CvDTree DecisionTree; typedef CvForestTree ForestTree; typedef CvRTParams RandomTreeParams; typedef CvRTrees RandomTrees; typedef CvERTreeTrainData ERTreeTRainData; typedef CvForestERTree ERTree; typedef CvERTrees ERTrees; typedef CvBoostParams BoostParams; typedef CvBoostTree BoostTree; typedef CvBoost Boost; typedef CvANN_MLP_TrainParams ANN_MLP_TrainParams; typedef CvANN_MLP NeuralNet_MLP; typedef CvGBTreesParams GradientBoostingTreeParams; typedef CvGBTrees GradientBoostingTrees; template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj(); CV_EXPORTS bool initModule_ml(void); }
三,总结
opencv_ml模块中包含一些常见的机器学习算法,集成了一些目前比较优秀的算法库如libsvm等。不仅可以用于图像,也可以用于其他问题中。