问题
I am quite new to both OpenCV and Support Vector Machines. I want to use SVM to train a dataset with two labels and then predict the label of a given set. My current set contains about 600 rows with equal class distributions (300 for 1 and 300 for -1) containing 34 columns.
This is my current code for setting up OpenCV's SVM. I am using OpenCV 3.0.0
// trainingData is an int array with size 600x34
// labels is an int array with size 600, they're the labels corresponding to the trainingData rows
cv::Mat trainingDataMat(600, 34, CV_32FC1, trainingData);
cv::Mat labelsMat(600, 1, CV_32SC1, labels);
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
cv::Ptr<cv::ml::TrainData> tempData = cv::ml::TrainData::create(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
svm->setType(cv::ml::SVM::C_SVC);
svm->setKernel(cv::ml::SVM::RBF);
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 100, 0.001));
// Assign the SVM parameters to the most accurate result
svm->trainAuto(tempData);
// Train the SVM
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
// predictRow contains a row of data with 34 columns to predict against the SVM Model
cv::Mat sampleMat(1, 34, CV_32FC1, predictRow);
// Prediction
float response = svm->predict(sampleMat);
std::cout << response << std::endl;
The SVM training seems to work fine. But when I predict a row, response always shows "1" no matter how the input looks like. Even when I try to predict using training rows with "-1" label I used earlier, the response is still "1". I tried to increase the max iteration parameter for the termination criteria to a large number. The training process takes more time but the results are still the same.
I tried to use the libsvm library (https://www.csie.ntu.edu.tw/~cjlin/libsvm/) to see if the same behavior occurs. Interestingly, it worked well. I use the Windows "svm-train.exe" and "svm-predict.exe" command to validate it and the responses are accurate. I even tried to run the executables on the OpenCV program by using some dirty system calls and file I/O. The resulting responses using the training rows are correct.
I suspect there is something wrong with the my SVM parameters. Even by using train_auto function, the SVM model still shows strange behaviour. I wonder if anyone can help me setting the SVM parameters correctly in OpenCV 3.0?
来源：https://stackoverflow.com/questions/33699200/opencv-svm-gives-strange-prediction-results