Hi Jordi and Benjamin,
If you read it in the code, then it must be true ... I am pretty sure we
had this tested but it seems not according to [1]. I remember
discussions about this when we developed the OpenCV ML models, and I
found the following Jira task and discussion about it [2].
But I would need some hint from the development team on this issue.
Whatever comes out, this is at least worth a bug report.
Regards,
Julien
[1]
http://hg.orfeo-toolbox.org/OTB/file/9890c6c5f335/Testing/Code/Learning/otbTrainMachineLearningModel.cxx
[2] http://scrum.orfeo-toolbox.org/jira/browse/OTB-454
Le 25/03/2014 15:37, Jordi Inglada a écrit :
Jordi Inglada <jordi.inglada-L4RxXcqyP7Z0EDqhht/[email protected]> wrote:
Hi,
I have had a look at the code, and in otbSVMMachineLearningModel.txx (about
line 79) we have this:
// Train the SVM
if (!m_ParameterOptimization)
{
m_SVMModel->train(samples, labels, cv::Mat(), cv::Mat(), params);
}
else
{
//Trains SVM with optimal parameters.
//train_auto(const Mat& trainData, const Mat& responses, const Mat& varIdx,
const Mat& sampleIdx,
//CvSVMParams params, int k_fold=10, CvParamGrid
Cgrid=CvSVM::get_default_grid(CvSVM::C),
//CvParamGrid gammaGrid=CvSVM::get_default_grid(CvSVM::GAMMA),
//CvParamGrid pGrid=CvSVM::get_default_grid(CvSVM::P), CvParamGrid
nuGrid=CvSVM::get_default_grid(CvSVM::NU),
//CvParamGrid coeffGrid=CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid
degreeGrid=CvSVM::get_default_grid(CvSVM::DEGREE),
//bool balanced=false)
//We used default parameters grid. If not enough, those grids should be
expose to the user.
m_SVMModel->train_auto(samples, labels, cv::Mat(), cv::Mat(), params);
}
So the train_auto method is called and it should work. However, reading the
OpenCV documentation
(http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm) I
see this:
"If there is no need to optimize a parameter, the corresponding grid step should be
set to any value less than or equal to 1. For example, to avoid optimization in gamma,
set gamma_grid.step = 0, gamma_grid.min_val, gamma_grid.max_val as arbitrary numbers. In
this case, the value params.gamma is taken for gamma."
I understand that if the grid steps are < 1 there is no optimization. And the
default constructor for the parameter grids is this:
CvParamGrid::CvParamGrid()
{
min_val = max_val = step = 0;
}
So I guess that the optimization is not done.
Can anybody confirm this hypothesis?
To add some information, Benjamin has used libSVM instead of OpenCV's SVM (just
choosing the other option in the application) and the parameter optimization
works.
Is this a big or are we doing something wrong?
Thanks.
Jordi
Thank you.
Jordi
Benjamin Tardy
<tardybenjamin4-re5jqeeqqe8avxtiumwx3w-xmd5yjdbdmrexy1tmh2...@public.gmane.org>
wrote:
Hello,
I'm trying to use TrainImageClassifier Application, with svm model and
optimization.
OTB Version:3.18.1
I set all parameters with SetParameter...:
model= otb.Registry.CreateApplication("TrainImagesClassifier")
model.SetParameterStringList("io.il","im.tif")
model.SetParameterStringList("io.vd","training.shp")
model.SetParameterString("io.imstat","stats.xml")
model.SetParameterString("io.confmatout","mat.csv")
model.SetParameterString("io.out","model.svm")
model.SetParameterFloat("sample.vtr",0.5)
model.SetParameterString("sample.vfn","Class")
model.SetParameterString("classifier","svm")
model.SetParameterString("classifier.svm.m","csvc")
model.SetParameterString("classifier.svm.k","rbf")
model.SetParameterFloat("classifier.svm.c",1)
model.SetParameterFloat("classifier.svm.gamma",1)
model.SetParameterInt("rand",3)
model.SetParameterInt("classifier.svm.opt",0)
model.ExecuteAndWriteOutput()
This program works and gives results. But when I change the classifier.svm.opt
to 1: I got same results and
confusion matrix.
I try to use model.SetParameterString("classifier.svm.opt",true) and false, but
nothing change.
I use the command line application too, with parameter 0,1 and true,false,and
change default values for c and
gamma ( 1 to 100000 or 0.5), always same results...
Any help is welcome
Thank you,
Benjamin
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