OpenCV-SVM总是预测更高级的标签

我使用OpenCV SVM实现来二进制预测图像特征的重要性。因此，我在正图像特征和负图像特征上训练它，并在{0,1}中寻找分类。

我遇到的问题是，在训练之后，SVM总是预测具有更高/更大类别标签的类别。我可以更改训练数据集的标签，但此问题仍然存在。我仔细检查了生成的标签和培训cv:：Mat矩阵，没有发现任何问题。

下面是我的SVM类和附带的SVM参数

//Populate the SVM parameters
void SVM::setSVMParams()
{
    params.svm_type = cv::SVM::C_SVC;
    params.kernel_type = cv::SVM::RBF;
    params.term_crit = cv::TermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
    params_set = true;
}
//Train the SVM with the given data
void SVM::train(cv::Mat train_data, cv::Mat labels)
{
    //Set the SVM parameters if they haven't been already
    if (!params_set)
    {
        setSVMParams();
    }
    svm.train(train_data, labels, cv::Mat(), cv::Mat(), params);
}
//Based on training, predict the class of the given data
float SVM::predict(cv::Mat sample)
{
    return svm.predict(sample, false);
}

这里是负责生成训练数据和相应标签的功能

//Creates the appropriate training data and class labels for subsequent SVM training according to supplied D threshold
void Matchings::createSVMTrainingObjects(const float t_D, const float positive_label, const float negative_label, bool print_info)
{
    cv::Mat train_data_l((int)matchings_list.size(), 132, CV_32FC1);
    cv::Mat labels_l((int)matchings_list.size(), 1, CV_32FC1);
    int num_pos = 0;
    int num_neg = 0;
    for (int i = 0; i < matchings_list.size(); i++)
    {
        matching_d entry = matchings_list[i];
        //Important feature, label 1
        if (entry.D > t_D)
        {
            labels_l.at<float>(i) = positive_label;
            num_pos++;
        }
        //Unimportant feature, label -1
        else
        {
            labels_l.at<float>(i) = negative_label;
            num_neg++;
        }
        int j = 0;
        //Copy feature into current row of openCV matrix
        train_data_l.at<float>(i, j++) = entry.feature.x;
        train_data_l.at<float>(i, j++) = entry.feature.y;
        train_data_l.at<float>(i, j++) = entry.feature.scale;
        train_data_l.at<float>(i, j++) = entry.feature.angle;
        for (int k = 0; k < 128; k++)
        {
            train_data_l.at<float>(i, j + k) = entry.feature.vec[k];
        }
    }
    std::cout << "For training: #+ves=" << num_pos << ", #-ves=" << num_neg << std::endl;
    train_data = train_data_l;
    labels = labels_l;
}

最后，这里是一个函数，它实际调用SVM预测结果来保留重要的图像特征

matchingslist ASIFT::filterFeaturesWithSVM(matchingslist matchings, SVM& svm)
{
    matchingslist new_matchings;
    for (int i = 0; i < (int)matchings.size(); i++)
    {
        cv::Mat first = Utility::keypointToMat(matchings[i].first);
        cv::Mat second = Utility::keypointToMat(matchings[i].second);
        //If both features are of importance, retain them
        if (svm.predict(first) == 1.0f && svm.predict(second) == 1.0f)
        {
            new_matchings.push_back(matchings[i]);
        }
        else
        {
            std::cout << "Feature removed" << std::endl;
        }
    }
    return new_matchings;
}