opencv - 使用功能包进行分类的词汇/字典

Question

我正在尝试使用SVM进行3类分类。为此，我正在SVM培训期间准备词汇表。但是，由于我在SVM预测期间获得随机结果，因此我怀疑我的词汇创建方法中存在一些问题。我创建词汇的代码如下:

//Mat train --- it should contain the feature vectors
//Mat response-- it will contain the class labels

void svm::createTrainingDateUsingBOW(int flag,Mat& train, Mat& response, int label)
{

    int cluster = 9; // Common for all classes  
    cv::Mat imageForTraining;
    std::vector<cv::KeyPoint> keypoints; 
    cv::SurfFeatureDetector detector(500);
    cv::Ptr<cv::DescriptorExtractor> cvDescExt = new cv::SurfDescriptorExtractor();
    cv::Mat descriptors; 

    cv::BOWKMeansTrainer bow(cluster, cv::TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, FLT_EPSILON), 1, cv::KMEANS_PP_CENTERS);
    cv::Mat vocabulary; 

    if (flag == 1)
    {
        for(int i=1; i<=400; i++)
        {
            counter++;
            cout<<"\n counter:  "<<counter;

            char filepath[255];
            sprintf(filepath, "class1/%d.JPG",i); // we need class1/1.JPG etc

            imageForTraining = cv::imread(filepath, CV_LOAD_IMAGE_GRAYSCALE);

            // Preparing keypoints using detector
            detector.detect(imageForTraining, keypoints);

            // now getting the DESCRIPTORS for the given keypoints
            cvDescExt->compute(imageForTraining, keypoints, descriptors);

            //BOW
            if(keypoints.size() > cluster)  // so that (N<k) error won't come
            {
                if (!descriptors.empty()) bow.add(descriptors); 

                //VOCABULARY
                vocabulary = bow.cluster();

                cv::Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
                cv::Ptr<DescriptorMatcher> matcher = cv::DescriptorMatcher::create("FlannBased");
                cv::BOWImgDescriptorExtractor descExtractor (extractor, matcher);

                descExtractor.setVocabulary(vocabulary); 
                Mat bowDescriptors; 
                descExtractor.compute(imageForTraining, keypoints, bowDescriptors); 
                if ( !bowDescriptors.empty())
                {
                    train.push_back(bowDescriptors);
                    response.push_back(label);
                }

            }// ending if loop

        } // ending For loop    
    }// ending if(flag ) loop


    if (flag == 2)
    {
        for(int i=1; i<=400; i++)
        {
            counter++;
            cout<<"\n counter:  "<<counter;

            char filepath[255];
            sprintf(filepath, "class2/%d.JPG",i); // we need class1/1.JPG etc

            imageForTraining = cv::imread(filepath, CV_LOAD_IMAGE_GRAYSCALE);

            // Preparing keypoints using detector
            detector.detect(imageForTraining, keypoints);

            // now getting the DESCRIPTORS for the given keypoints
            cvDescExt->compute(imageForTraining, keypoints, descriptors);

            //BOW
            if(keypoints.size() > cluster)  // so that (N<k) error won't come
            {
                if (!descriptors.empty()) bow.add(descriptors); 

                //VOCABULARY
                vocabulary = bow.cluster();

                cv::Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
                cv::Ptr<DescriptorMatcher> matcher = cv::DescriptorMatcher::create("FlannBased");
                cv::BOWImgDescriptorExtractor descExtractor (extractor, matcher);

                descExtractor.setVocabulary(vocabulary); 
                Mat bowDescriptors; 
                descExtractor.compute(imageForTraining, keypoints, bowDescriptors); 
                if ( !bowDescriptors.empty())
                {
                    train.push_back(bowDescriptors);
                    response.push_back(label);
                }

            }// ending if loop

        } // ending For loop    
    }// ending if(flag ) loop

....Similary for Class-3

}

Answer 1

您应该在词典中包含所有词汇。
这意味着您应该一次循环播放所有图片。