Thank you very much for your response.
regarding first question:
Do you save and/or load the training data in-between the training and classification stages?
Code: Select all
nodeTrainer->train();
nodeTrainer->save("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\nodetrainn.dat");
nodeTrainer->reset();
nodeTrainer->load("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\nodetrainn.dat");
edgeTrainer->train();
Timer::stop();
// ==================== STAGE 3: Filling the Graph =====================
Timer::start("Filling the Graph... ");
// CV_32FC(nStates) <- CV_8UC(nFeatures);
Mat nodePotentials = nodeTrainer->getNodePotentials(test_fv); // Classification: CV_32FC(nStates) <- CV_8UC(nFeatures)
Serialize::to("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\fileName.dat", nodePotentials);
graph->setNodes(nodePotentials); // Filling-in the graph nodes
graph->fillEdges(edgeTrainer, test_fv, params, params_len); // Filling-in the graph edges with pairwise potentials
Timer::stop();
regarding second question:
The error occurs on the first iteration or after some iteration?
When I choose the below three sections in the node-training-model, the decoding part does not work properly. It seems that after several iteration the error appears.
section 1: Gaussian Mixture model
section 3: nearest neighbor
section 5: Microsoft random Forrest.
regarding third question:
Which inference method do you use? Have you tried running different inference methods?
I explored different combination of node taring models with edge training models. It seems that when the number of features is more than 5, GMM, k-nn, mRF do not work. I tested all of these combination with 1D feature . All of them work when the feature is 1D or 3D.
Could you please check that the node potentials, used in the graph filling stage are not empty?
nodePotentials is not empty.
Which node and which edge training models do you use?
node-training-model: OpenCV Gaussian Mixture Model
edge-training-model: Concatenated Model
the above models do not produce errors for 5-D feature.
You wrote that you have checked the features. Have you also checked the responses in
Code: Select all
Mat data2 = raw_data->getResponses();
In particular for the class, starting from which, the bug appears?
When I choose node training model as
section 1: Gaussian Mixture model
section 3: nearest neighbor
section 5: Microsoft random Forrest.
and when I use a feature matrix with higher dimensions.
Thank you very much for your response.
regarding first question:
[quote]Do you save and/or load the training data in-between the training and classification stages?
[/quote]
[code]
nodeTrainer->train();
nodeTrainer->save("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\nodetrainn.dat");
nodeTrainer->reset();
nodeTrainer->load("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\nodetrainn.dat");
edgeTrainer->train();
Timer::stop();
// ==================== STAGE 3: Filling the Graph =====================
Timer::start("Filling the Graph... ");
// CV_32FC(nStates) <- CV_8UC(nFeatures);
Mat nodePotentials = nodeTrainer->getNodePotentials(test_fv); // Classification: CV_32FC(nStates) <- CV_8UC(nFeatures)
Serialize::to("E:\\zghassabi\\Project1-DIE\\Dataset\\image 1\\fileName.dat", nodePotentials);
graph->setNodes(nodePotentials); // Filling-in the graph nodes
graph->fillEdges(edgeTrainer, test_fv, params, params_len); // Filling-in the graph edges with pairwise potentials
Timer::stop();
[/code]
regarding second question:
[quote]The error occurs on the first iteration or after some iteration? [/quote]
When I choose the below three sections in the node-training-model, the decoding part does not work properly. It seems that after several iteration the error appears.
section 1: Gaussian Mixture model
section 3: nearest neighbor
section 5: Microsoft random Forrest.
regarding third question:
[quote]Which inference method do you use? Have you tried running different inference methods? [/quote]
I explored different combination of node taring models with edge training models. It seems that when the number of features is more than 5, GMM, k-nn, mRF do not work. I tested all of these combination with 1D feature . All of them work when the feature is 1D or 3D.
[quote]Could you please check that the node potentials, used in the graph filling stage are not empty?[/quote]
nodePotentials is not empty.
[quote]Which node and which edge training models do you use? [/quote]
node-training-model: OpenCV Gaussian Mixture Model
edge-training-model: Concatenated Model
the above models do not produce errors for 5-D feature.
[quote]You wrote that you have checked the features. Have you also checked the responses in
[code]
Mat data2 = raw_data->getResponses();
[/code] In particular for the class, starting from which, the bug appears? [/quote]
When I choose node training model as
section 1: Gaussian Mixture model
section 3: nearest neighbor
section 5: Microsoft random Forrest.
and when I use a feature matrix with higher dimensions.