Question

我试图下载一套用于功能袋的代码。并尝试了一下，尽管我建议将代码与原始代码（使用SURF检测器和提取器）对比使用SIFT检测器。我的词汇发现中存在kmeans的一些未知问题阶段。该程序会自行终止，并在某个内存位置声明未知源

有人可以帮助检查代码有什么问题吗？我试图逐行编码出另一套特征编码袋。这次没有这样的问题。

我也可以查询

kmeans可以处理的样本数量和维数是否有限制
在功能/单词袋中，我应该使用完整的训练集来训练词汇表还是字典，还是应该仅使用表示形式（意味着训练集中的样本）来形成词汇表？

谢谢

bool createVocabulary(const DatabaseType& trainingDb, 
                      cv::Mat& outVocabulary)
{
    CV_Assert(!trainingDb.empty());

    cv::Ptr<cv::xfeatures2d::SIFT> detector =  cv::xfeatures2d::SIFT::create();
    cv::Ptr<cv::DescriptorExtractor> extractor = cv::xfeatures2d::SIFT::create();

    cv::Mat trainingDescriptors(1, extractor->descriptorSize(), extractor->descriptorType());

    outVocabulary.create(0, 1, CV_32FC1);

    int counter = 0;
    for (auto& it : trainingDb)
    {
        std::vector<cv::KeyPoint> keypoints;
        detector->detect(it.second, keypoints);

        if (!keypoints.empty())
        {
            cv::Mat descriptors;
            extractor->compute(it.second, keypoints, descriptors);

            if (!descriptors.empty())
            {
                std::cout << "Image " << counter
                     << "- Adding " << descriptors.rows << " training descriptors." << std::endl;
                 counter++;
                 trainingDescriptors.push_back(descriptors);
            }
            else
            {
                std::cout << "- No descriptors found." << std::endl;
            }
        }
        else
        {
             std::cout << "- No keypoints found." << std::endl;
        }

    }

    if (trainingDescriptors.empty())
    {
        std::cout << "- Training descriptors are empty." << std::endl;
        return false;
    }

    std::cout << "Total Descriptors extracted " << trainingDescriptors.rows << std::endl;

    //Construct BOWKMeansTrainer
    //the number of bags
    int dictionarySize=200;
    //define Term Criteria
    cv::TermCriteria tc(CV_TERMCRIT_ITER,100,0.001);
    //retries number
    int retries= 3;
    //necessary flags
    int flags=cv::KMEANS_PP_CENTERS;

    //Create the BoW (or BoF) trainer
    cv::BOWKMeansTrainer bowTrainer(dictionarySize,tc,retries,flags);


     bowTrainer.add(trainingDescriptors);

     //cluster the feature vectors
     outVocabulary=bowTrainer.cluster();

     return true;
}

单词/功能袋中的kmeans问题

0 个答案: