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Comparing KMeans centers for the best match

asked 2015-07-14 15:32:53 -0500

albertJ gravatar image

updated 2015-07-14 17:11:18 -0500

I'm working on implementing the VZ classifier (, 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++) {<float>(y, x) =<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 <<<float>(0) << endl; cout << "And the full cluster: " << endl << centers3 << endl; waitKey(); Mat centers1 = Mat::eye(floatImg.rows,floatImg.cols,CV_32S);


Mat centers2 = createSamples(floatImg, centers1, 5); cout << "This is the cluster centers of the second kmeans: " << endl <<<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!

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imho, your kmeansFunc() and createSamples() look quite broken (if you'd run a debug build, you'd get a ton of runtime errors) better don't try to preallocate the centers(wrong shape, anyway) or use initial labels (Mat::eye is the wrong way to calculate them)

the input to kmeans is a Mat where each row is a flattened output from the filterbank convolution, so nImages*nConvolutions rows, and img.rows*img.cols columns.

then the output from kmeans(centers) will be a Mat with 10 rows and img.rows*img.cols columns, too.


Mat kdata; // start empty
for each img from convolution:
      Mat flat = img.reshape(1,1);

Mat labels,centers; // empty
kmeans(kdata, 10, labels, TermCriteria(), 5, KMEANS_PP_CENTERS, centers);
berak gravatar imageberak ( 2015-07-15 00:47:37 -0500 )edit

thanks berak, yeah i meant to clean it up, although haven't found a good debugger for atom yet.

-Interesting about kmeans, can i create a single set of clusters from several images, the pseudo code above give me an error about the matrix not being continuous. would a continuous matrix of several images give the same centers as if i blended the same images together and ran it through kmeans?

-Also the key point, is there a way to find the nearest cluster centre for an image from a previous set of cluster centres?

albertJ gravatar imagealbertJ ( 2015-07-15 08:27:20 -0500 )edit

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answered 2015-08-15 12:13:44 -0500

albertJ gravatar image

For anyone looking for an answer or hints on this or specifically about being able to cluster multiple images easily, the way i found was using parts of the Bag of Words classes in the c++ api for opencv.


specifically will allow you to push back as multiple Mat images easily and and then generate a set of cluster centers using:


Definitely better than messing around trying to cluster them with the traditional kmeans function.

I haven't yet found a good solution to matching the cluster centres with the the generated vocabulary yet, as the other BOW opencv classes as far as i can tell wont work when using filterbanks. So i'll update it when i find something, otherwise you can check my current implementation on github:

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it would be nice if you could upload some images showing actually what the code in github implements, and even better if you are willing to make a tutorial or something ;-)

theodore gravatar imagetheodore ( 2015-08-15 12:57:33 -0500 )edit

Hi, yeah i'll be happy to do that when i get some more time, it's for my masters thesis so am rushing to finish it by september.

albertJ gravatar imagealbertJ ( 2015-08-18 15:14:24 -0500 )edit

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Asked: 2015-07-14 15:32:53 -0500

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Last updated: Aug 15 '15