OPENCV C 的SVM返回同一类图像的相同置信度得分

SVM for OpenCV C++ Returns Same Confidence Score for Same Class Images

本文关键字：图像 一类 SVM 返回 OPENCV      更新时间：2023-10-16

我已经训练了20堂课的SVM，现在为了新图像，我想首先将其分类为20个训练的班级之一，然后以其为基础显示置信得分分类。

问题：当我测试1类图像时，它们都返回了相同的置信度得分。我知道每个图像都应根据对其进行分类的分数不同。所以我知道我的代码中存在问题。

我什至更改了实例变量float *deciest_function在" OpenCV"框架上从受保护到公共并重建它，但问题仍未解决。

我想计算正确的分数，因为我以后要根据置信度分数对我的查询图像最匹配的图像。

请让我知道为什么我在同一班级中获得相同的置信度得分，而我的代码中的何处我犯了错误。

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased"); 
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
SurfFeatureDetector detector(500);// hemessian threshold
int dictionarySize = 100;  //number of clusters=100
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(extractor, matcher);
int main( int argc, char** argv )
{
    int i,j;
    Mat dictionary; // creating a dictionary
    FileStorage fs("dictionary_new.yml", FileStorage::READ);
    fs ["vocabulary"]>> dictionary;
    fs.release();
    bowDE.setVocabulary(dictionary);
    Mat labels(0, 1, CV_32FC1);  //to store labels of images
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;
    svm.load("svm_new.yml");
    cout<<"Processing evaluation data..."<<endl;
    string tags[] = {"none","Plane","Bike","Face","Car","Bag","binocular","Gloves","Bread_Maker","Revolver","Ring","Guitar","Elephant","boat","French_horn","Gorilla","Headphone","Flower","Penguin","Tiger","Laptop"};
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;
    Mat img2; //to load an image
    img2 = imread("3 (14).jpg");
    detector.detect(img2, keypoint2);
    bowDE.compute(img2, keypoint2, bowDescriptor2);
    evalData.push_back(bowDescriptor2);
    float response = svm.predict(bowDescriptor2, true);
    float confidence=0;
    int f_to_i=(int)response;
    confidence=1.0/(1.0+exp(-response));
    cout<<"nnIt's a "<<tags[f_to_i]<<" and its confidence: "<<confidence;
    _getch();
    return 0; 
}

计算得分：

1 (1).jpg,Plane,0.731059
1 (2).jpg,Plane,0.731059
1 (3).jpg,Plane,0.731059
1 (4).jpg,Plane,0.731059
1 (5).jpg,Plane,0.731059
1 (6).jpg,Plane,0.731059
1 (7).jpg,Plane,0.731059
1 (8).jpg,Plane,0.731059
1 (9).jpg,Plane,0.731059
1 (10).jpg,Plane,0.731059
1 (11).jpg,Plane,0.731059
1 (12).jpg,Plane,0.731059
1 (13).jpg,Plane,0.731059
1 (14).jpg,Plane,0.731059
1 (15).jpg,Plane,0.731059
1 (16).jpg,Plane,0.731059
1 (17).jpg,Plane,0.731059
1 (18).jpg,Plane,0.731059
1 (19).jpg,Plane,0.731059
1 (20).jpg,Plane,0.731059

我也有类似的问题。问题在于，当您有更多的2堂课时，您将无法通过SVM.Predict（Bowdescriptor2，true）获得响应。如果问题是多类OpenCV SVM，则仅返回类标签号。这就是解释为什么您拥有相同的值。

查看文档：http：//docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-predict

" returndfval - 指定返回值的类型。如果是true且问题是2级分类，则该方法返回签名距离的决策函数值，否则该功能返回类标签（分类）或估计的功能值（回归）。"

可能的解决方案是修改float cvsvm ::预测（const float* row_sample，int row_len，bool returndfval）方法。在那里您可以进行更改以获取每个班级的决策功能。

查看代码的一部分：... 

for (i = 0; i < class_count; i++)
{
    for (j = i + 1; j < class_count; j++, df++)
    {
        sum = -df->rho;
        int sv_count = df->sv_count;
        for (k = 0; k < sv_count; k++)
            sum += df->alpha[k] * buffer[df->sv_index[k]];
        vote[sum > 0 ? i : j]++;
    }
}
for (i = 1, k = 0; i < class_count; i++)
{
    if (vote[i] > vote[k])
        k = i;
}
result = returnDFVal && class_count == 2 ? (float)sum : (float)(class_labels->data.i[k]);
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