c++ - Recommended values for OpenCV SVM parameters -


any idea on recommended parameters opencv svm? i'm playing letter_recog.cpp in opencv sample directory, however, svm accuracy poor! in 1 run got 62% accuracy:

$ ./letter_recog_modified -data /home/cobalt/opencv/samples/data/letter-recognition.data  -save svm_letter_recog.xml -svm  database /home/cobalt/opencv/samples/data/letter-recognition.data loaded. training classifier ... data.size() = [16 x 20000] responses.size() = [1 x 20000]  recognition rate: train = 64.3%, test = 62.2%

the default parameters are:

model = svm::create(); model->settype(svm::c_svc); model->setkernel(svm::linear); model->setc(1); model->train(tdata);

setting trainauto() didn't help; gave me weird 0 % test accuracy:

model = svm::create(); model->settype(svm::c_svc); model->setkernel(svm::linear); model->trainauto(tdata);

result:

recognition rate: train = 0.0%, test = 0.0%

update using yangjie's answer:

$ ./letter_recog_modified -data /home/cobalt/opencv/samples/data/letter-recognition.data  -save svm_letter_recog.xml -svm database /home/cobalt/opencv/samples/data/letter-recognition.data loaded. training classifier ... data.size() = [16 x 20000] responses.size() = [1 x 20000]  recognition rate: train = 58.8%, test = 57.5%

the result no longer 0% accuracy worse 62% earlier.

using rbf kernel trainauto() worst?

$ ./letter_recog_modified_rbf -data /home/cobalt/opencv/samples/data/letter-recognition.data  -save svm_letter_recog.xml -svm database /home/cobalt/opencv/samples/data/letter-recognition.data loaded. training classifier ... data.size() = [16 x 20000] responses.size() = [1 x 20000]  recognition rate: train = 18.5%, test = 11.6%

parameters:

    model = svm::create();     model->settype(svm::c_svc);     model->setkernel(svm::rbf);     model->trainauto(tdata);

i debugged sample code , found reason.

the responses mat of ascii code of letters.

however, predicted labels returned svm trained svm::trainauto ranging 0-25, correspond 26 classes. can observed looking @ <class_labels>...</class_labels> in output file svm_letter_recog.xml.

therefore in test_and_save_classifier, r = model->predict( sample ) , responses.at<int>(i) apparently not equal.

i found if use svm::train, class labels 65-89 instead, why can normal result @ first.

solution

i not sure whether bug. if want use svm::trainauto in sample now, can change

test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save);

in build_svm_classifier to

test_and_save_classifier(model, data, responses, ntrain_samples, 'a', filename_to_save);

update

trainauto , train should have same behavior in class_labels. problem due bug fix before. have created pull request opencv fix problem.


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