Input matrix to opencv kmeans clustering

Question

This question is specific to opencv: The kmeans example given in the opencv documentation has a 2-channel matrix - one channel for each dimension of the feature vector. But, some of the other example seem to say that it should be a one channel matrix with features along the columns with one row for each sample. Which of these is right?

if I have a 5 dimensional feature vector, what should be the input matrix that I use: This one:

cv::Mat inputSamples(numSamples, 1, CV32FC(numFeatures))

or this one:

cv::Mat inputSamples(numSamples, numFeatures, CV_32F)

Answer 1

The correct answer is cv::Mat inputSamples(numSamples, numFeatures, CV_32F). The OpenCV Documentation about kmeans says:

samples – Floating-point matrix of input samples, one row per sample

So it is not a Floating-point vector of n-Dimensional floats as in the other option. Which examples suggested such a behaviour?

Here is also a small example by me that shows how kmeans can be used. It clusters the pixels of an image and displays the result:

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

using namespace cv;

int main( int argc, char** argv )
{
  Mat src = imread( argv[1], 1 );
  Mat samples(src.rows * src.cols, 3, CV_32F);
  for( int y = 0; y < src.rows; y++ )
    for( int x = 0; x < src.cols; x++ )
      for( int z = 0; z < 3; z++)
        samples.at<float>(y + x*src.rows, z) = src.at<Vec3b>(y,x)[z];


  int clusterCount = 15;
  Mat labels;
  int attempts = 5;
  Mat centers;
  kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10000, 0.0001), attempts, KMEANS_PP_CENTERS, centers );


  Mat new_image( src.size(), src.type() );
  for( int y = 0; y < src.rows; y++ )
    for( int x = 0; x < src.cols; x++ )
    { 
      int cluster_idx = labels.at<int>(y + x*src.rows,0);
      new_image.at<Vec3b>(y,x)[0] = centers.at<float>(cluster_idx, 0);
      new_image.at<Vec3b>(y,x)[1] = centers.at<float>(cluster_idx, 1);
      new_image.at<Vec3b>(y,x)[2] = centers.at<float>(cluster_idx, 2);
    }
  imshow( "clustered image", new_image );
  waitKey( 0 );
}