SURF matching against a database of images

edit

Hi again: If i change

//At this point, we should to build the index of the big  matrix
Index fln = new Index(supermatrix, 4);

//Retrieve The Rows supermatrix
Matrix<int> indices = new Matrix<int>(supermatrix.Rows, k);
Matrix<float> dist = new Matrix<float>(supermatrix.Rows, k);

fln.KnnSearch(observedDescriptors, indices, dist, k, 12);

to

//At this point, we should to build the index of the big  matrix
Index fln = new Index(observedDescriptors, 4);

//Retrieve The Rows supermatrix

Matrix<int> indices = new Matrix<int>(supermatrix.Rows, k);
Matrix<float> dist = new Matrix<float>(supermatrix.Rows, k);

fln.KnnSearch(supermatrix, indices, dist, k, 20);

I think that this line it's incorrect, i think that the index should be built with supermatrix

Index fln = new Index(observedDescriptors, 4); //What it's the correct line?
Index fln = new Index(supermatrix, 4);

Indices and dist have values but the following code has output many rare values:

 for (int i = 0; i < indices.Rows; i++){                
           if (dist.Data[i, 0] < 0.8 * dist.Data[i, 1]){
                        Debug.WriteLine("This Point Is Good");
                        Debug.WriteLine("The image most similar is" + i);
         }
    }


This Point Is Good
The image most similar is11
This Point Is Good
The image most similar is12
This Point Is Good
The image most similar is25
This Point Is Good
The image most similar is27
This Point Is Good
The image most similar is30
This Point Is Good
The image most similar is31
................................
................................

All points in dist are similar?
I think that i have a big error in the code.

I modified the code but i have retrieve the same result. Indices and dist are always contains 0 values in The system never executed the line " Debug.WriteLine("This Point Is Good"); I will be very gratefull if anyone could to fix my code. I am very lost with knntree examples. The second version of the code with the Guanta fix is:

    SURFDetector surfCPU = new SURFDetector(50, false);
    Matrix<float> matrix,supermatrix;
    VectorOfKeyPoint observedKeyPoints, modelKeyPoints;
    Image<Gray, byte> observedImage, modelImage;
    int k = 2;

    private void Pruebas(){
        //Load Image To Compare With ALL Images in Database
        observedImage = new Image<Gray, byte>(@"C:\Users\gabri_000\Pictures\uneuro.png");
        observedKeyPoints = surfCPU.DetectKeyPointsRaw(observedImage, null);
        Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);

        //Path With ALL Images Database
        //We will to build the big index for use with flann algorithm
        string[] images = Directory.GetFiles(@"C:\Users\gabri_000\Pictures", "*.png");
        foreach (string image in images)
        {
            DateTime start = DateTime.Now;
            Debug.WriteLine("Current Image  " + image);
            modelImage = new Image<Gray, byte>(image);

            modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
            matrix = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);

            if (supermatrix == null)
            {
                supermatrix = new Matrix<float>(matrix.Rows, matrix.Cols);
                supermatrix = matrix;
            }
            else
            {
                supermatrix= supermatrix.ConcateVertical(matrix);
            }


            DateTime end = DateTime.Now;

            Debug.WriteLine("Process Time " + (end - start));

        }

        //At this point, we should to build the index of the big  matrix
        Index fln = new Index(supermatrix, 4);

        //Retrieve The Rows supermatrix

        Matrix<int> indices = new Matrix<int>(supermatrix.Rows, k);
        Matrix<float> dist = new Matrix<float>(supermatrix.Rows, k);


        fln.KnnSearch(observedDescriptors, indices, dist, k, 12);

        Debug.WriteLine("-----------------------");

        for (int i = 0; i < indices.Size.Height; i++)
        {
            if (dist.Data[i, 0] < 0.6 * dist.Data[i, 1])
            {
                Debug.WriteLine("This Point Is Good");
                Debug.WriteLine("The image most similar is" + i);
            }

        }


    }

Your approach should work fine. So, in your set-up sth must be wrong. And yes, this is definitely possible with OpenCV. If you'd like to get the maximum performance build your index with the original-flann from http://www.cs.ubc.ca/~mariusm/index.php/FLANN/ with OpenMP-support and use OpenCV's GPU::SURF module (http://docs.opencv.org/modules/nonfree/doc/feature_detection.html#gpu-surf-gpu).

However note that depending on the number of SURF descriptors (which depend of course on the SURF-settings and the size of the images and their content) you'll have to do many comparisons and a lot of memory to build your flann-index. So, I suggest to go a different way and compute a single descriptor for your images either by a global image descriptor (see e.g. http://answers.opencv.org/question/9271/global-image-feature-implementation/) or by the usage of a bag-of-words-descriptor (see http://answers.opencv.org/question/8677/image-comparison-with-a-database). The drawback of the bag-of-words-descriptor is that you need to build a vocabulary first which will also take quite a while for such a database. However the results should be definitely better with less false positives and the cool thing with a global image descriptor is that you can use it for classification tasks.