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compare filter response kmeans center points to previous centers

I'm working on implementing the VZ classifier (http://www.robots.ox.ac.uk/~vgg/research/texclass/with.html), and am having problems creating the models.

The first stage is to cluster a large number of filter responses to generate a texton dictionary, where every texton is a kmeans cluster center.

Then i need to get filter responses from a test image then get a single kmeans cluster center from each response, marking what is the closest texton(archived cluster center) to the new center.

Then i'll display or store a histogram showing which textons were used and how frequently from the training images filter responses.

From what i've read labels seem promising, although i dont know i could input the whole texton database and work out which best fitted image.

Below is small bit of code i was using to try feeding the centers from one kmeans into the labels for a second kmeans on the same image. I was hoping that this could compare the cluster centers and show they're the same. (seemed logical at the time, have been stuck on this for a while..).


include "opencv2/core/core.hpp"
include "stdio.h"
include "string"
include "iostream"
include "opencv2/imgproc/imgproc.hpp"
include "opencv2/highgui/highgui.hpp"
 using namespace cv;
using namespace std;
 Mat kmeansFunc(Mat samples, Mat labels, int clusterCount) {
  // KMeans parameters
  int attempts = 5;
  Mat centers= Mat::zeros(2,2,CV_32F);
  // Apply KMeans
  kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10000, 0.0001), attempts, KMEANS_USE_INITIAL_LABELS, centers);
  return centers;
}
 Mat createSamples(Mat src, Mat labels, int clusterCount) {
  // Create sampleMat, same size as input image
  Mat samples(src.rows * src.cols, 1, CV_32F);
  // Copy across input image
  for (int y = 0; y < src.rows; y++) {
    for (int x = 0; x < src.cols; x++) {
      samples.at<float>(y, x) = src.at<float>(y, x);
    }
  }
  Mat labels1 = Mat::eye(samples.rows,samples.cols,CV_32S);
 // Put combined Mat through kmeans
  return kmeansFunc(samples, labels1, clusterCount);
}
 int main(int argc, char** argv) {
 /------------------------- Load Image ---------------------------/
  Mat img;
 if(argc !=2) {
    cout << "img path name not found. Exiting." << endl;
    return -1;
  }
 cout << "importing File: " << argv[1] << endl;
  // Read in image
 Mat in = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
  // Equalise image
  equalizeHist(in, img);
  waitKey();
 /--------------------- Cluster image --------------------------/
  Mat labels, floatImg;
 // Convert input image to a float
  img.convertTo(floatImg, CV_32F);
 Mat centers3 = createSamples(floatImg, labels, 4);
  cout << "This is the cluster centers of the first kmeans: " << endl << centers3.at<float>(0) << endl;
  cout << "And the full cluster: " << endl << centers3 << endl;
  waitKey();
  Mat centers1 = Mat::eye(floatImg.rows,floatImg.cols,CV_32S);
 //centers3.convertTo(centers1,CV_32S);
 Mat centers2 = createSamples(floatImg, centers1, 5);
  cout << "This is the cluster centers of the second kmeans: " << endl << centers3.at<float>(0) << endl;
  cout << "And the full cluster: " << endl << centers3 << endl;
 //Mat centers3 = createSamples(floatImg, labels);
 // FileStorage fs("clusterCenters.xml", FileStorage::WRITE);
  // fs << "clusters" << centers3;
  // fs.release();
  // cout << "finished saving clusteres. Exiting." << endl;
 return 0;
}

Any help would be really appreciated. Thanks!

compare filter response kmeans center points to previous centersComparing KMeans centers for the best match