Cannot make EmguCV-OpenCV BOW Categorization work properly

edit

I have already asked here, but no one answered: http: //stackoverflow.com/questions/41724448/cannot-make-emgucv-opencv-bow-categorization-work-properly

I am trying to learn BOW object categorization. I have tried to implement the example given in the book "Practical OpenCV, Samarth Brahmbhatt" Chapter 8 (page 148)

• When I save the SVM's to file on the training stage and read them on the categorization stage, the result is comletely different. (If the line svm = notFromFile[category]; is removed, the results are wrong; if not, it is successful with the dataset provided by the book.)
• When I try this code with some larger datasets, I sometimes get this exception: System.AccessViolationException' in Emgu.CV.World.dll for the line bowDescriptorExtractor.Compute(frame_g, kp, img); and the application closes. It cannot be handled.

I have tried many things but could not figure them out. Any suggestions why these are happening, and how to solve, is very appreciated.

I am using emgucv-windesktop 3.1.0.2504

My implementation:

internal class Categorizer3 : ICategorizer
{
    public string Name
    {
        get
        {
            return "Categorizer3";
        }
    }

    public bool Train()
    {
        try
        {
            initDir();
            Feature2D descriptorExtractor;
            Feature2D featureDetector;
            List<Mat> templates;
            BOWKMeansTrainer bowtrainer;
            BOWImgDescriptorExtractor bowDescriptorExtractor;
            init(out descriptorExtractor, out featureDetector, out templates, out bowtrainer, out bowDescriptorExtractor);

            List<Tuple<string, Mat>> train_set;
            List<string> category_names;
            make_train_set(out train_set, out category_names);

            Mat vocab;
            build_vocab(descriptorExtractor, featureDetector, templates, bowtrainer, out vocab);

            bowDescriptorExtractor.SetVocabulary(vocab);

            Dictionary<string, Mat> positive_data;
            Dictionary<string, Mat> negative_data;
            make_pos_neg(train_set, bowDescriptorExtractor, featureDetector, category_names, out positive_data, out negative_data);

            this.train_classifiers(category_names, positive_data, negative_data);

            return true;
        }
        catch (Exception)
        {
            return false;
        }
    }
    public event TrainedEventHandler Trained;
    protected void OnTrained(string fn)
    {
        if (this.Trained != null)
            this.Trained(fn);
    }
    public Categorizer3()
    {
    }
    private Feature2D create_FeatureDetector()
    {
        return new SURF(500);
        //return new KAZE();
        //return new SIFT();
        //return new Freak();
    }
    private BOWImgDescriptorExtractor create_bowDescriptorExtractor(Feature2D descriptorExtractor)
    {
        LinearIndexParams ip = new LinearIndexParams();
        SearchParams sp = new SearchParams();
        var descriptorMatcher = new FlannBasedMatcher(ip, sp);

        return new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
    }
    private void init(out Feature2D descriptorExtractor, out Feature2D featureDetector, out List<Mat> templates, out BOWKMeansTrainer bowtrainer, out BOWImgDescriptorExtractor bowDescriptorExtractor)
    {
        int clusters = 1000;
        featureDetector = create_FeatureDetector();

        MCvTermCriteria term = new MCvTermCriteria(10000, 0.0001d);
        term.Type = TermCritType.Iter | TermCritType.Eps;
        bowtrainer = new BOWKMeansTrainer(clusters, term, 5, Emgu.CV.CvEnum.KMeansInitType.PPCenters);//****


        BFMatcher matcher = new BFMatcher(DistanceType.L1);//****
        descriptorExtractor = featureDetector;//******

        bowDescriptorExtractor = create_bowDescriptorExtractor(descriptorExtractor);


        templates = new List<Mat>();
        string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\templates";
        //string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";
        foreach (var filename in Directory.GetFiles(TEMPLATE_FOLDER, "*", SearchOption.AllDirectories))
        {
            templates.Add(GetMat(filename, true));
            this.OnTrained(filename);
        }
    }


    void make_train_set(out List<Tuple<string, Mat>> train_set, out List<string> category_names)
    {
        string TRAIN_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";

        category_names = new List<string>();
        train_set = new List<Tuple<string, Mat>>();
        foreach (var dir in Directory.GetDirectories(TRAIN_FOLDER))
        {
            // Get category name from name of the folder
            string category = new DirectoryInfo(dir).Name;
            category_names.Add(category);
            foreach (var filename in Directory.GetFiles(dir))
            {     
                train_set.Add(new Tuple<string, Mat>(category, GetMat(filename, true)));
                this.OnTrained(filename);
            }
        }
    }

    void build_vocab(Feature2D descriptorExtractor, Feature2D featureDetector, List<Mat> templates ...