CODEWITHSUNDEEP
opencvexpectation-maximization
Artoria
Artoria

Reputation: 31

Got different EM::predict() results after EM::read() saved model in OpenCV

I'm new to OpenCV and C++ and I'm trying to build a classifier using Gaussian Mixture Model within the OpenCV. I figured out how it works and got it worked ... maybe. I got something like this now:

If I classify the training samples just after the model was trained and saved, I got the result I want. But when I reclassify my training data using the read(), one of the clusters is missing, means I got different cluster result from the same GMM model. I don't get it now because the cluster I want was gone, I can't reproduce the classification again until I retrained the model using the same data. I checked the code in runtime and the result valule in the Vec2d from which predict() returned was never assigned to 1 (I set 3 clusters).

Maybe there's a bug or I did something wrong?

p.s. I'm using 2.4.8 in VS2013

My programs like this:

train part

void GaussianMixtureModel::buildGMM(InputArray _src){
    //use source to train GMM and save the model

    Mat samples, input = _src.getMat();    
    createSamples(input, samples);
    bool status = em_model.train(samples);

    saveModel();
}

save/load the model

FileStorage fs(filename, FileStorage::READ);
if (fs.isOpened()) // if we have file with parameters, read them
{
    const FileNode& fn = fs["StatModel.EM"];
    em_model.read(fn);
    fs.release();
}

FileStorage fs_save(filename, FileStorage::WRITE);
if (fs_save.isOpened()) // if we have file with parameters, read them
{
    em_model.write(fs_save);
    fs_save.release();
}

predict part

vector<Mat> GaussianMixtureModel::classify(Mat input){
/// samples is a matrix of channels x N elements, each row is a set of feature
Mat samples;
createSamples(input, samples);
    for (int k = 0; k < clusterN; k++){
        masks[k] = Mat::zeros(input.size(), CV_8UC1);
    }
int idx = 0;
for (int i = 0; i < input.rows; i++){
    for (int j = 0; j < input.cols; j++){
        //process the predicted probability
        Mat probs(1, clusterN, CV_64FC1);
        Vec2d response = em_model.predict(samples.row(idx++), probs);
        int result = cvRound(response[1]);
        for (int k = 0; k < clusterN; k++){
                            if (result == k){
                // change to the k-th class's picture
                masks[k].at<uchar>(i, j) = 255;
            }
                    ...
                    // something else
                    }
             }
       }
}

Upvotes: 2

Views: 933

Answers (1)

user4029127
user4029127

Reputation: 11

I suppose my answer will be too late but as I have encountered the same problem the solution I found may be useful for others.

By analysing the source code, I notice that in the case of EM::COV_MAT_DIAGONAL the eigen values of covariances matrix(covsEigenValues in source code) are obtained via SVD after loading the saved data. However, SVD computes the singular(eigen in our case)values and stores it in ASCENDING order. To prevent this , I simply extract directly the diagonal element of loaded covariance matrix in covsEigenValues to keep the good order.

Upvotes: 1

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