OpenCV 1.1 K-Means Clustering in High Dimensional Spaces

I am trying to write a bag of features system image recognition system. One step in the algorithm is to take a larger number of small image patches (say 7x7 or 11x11 pixels) and try to cluster them into groups that look similar. I get my patches from an image, turn them into gray-scale floating point image patches, and then try to get cvKMeans2 to cluster them for me. I think I am having problems formatting the input data such that KMeans2 returns coherent results. I have used KMeans for 2D and 3D clustering before but 49D clustering seems to be a different beast.

I keep getting garbage values for the returned clusters vector, so obviously this is a garbage in / garbage out type problem. Additionally the algorithm runs way faster than I think it should for such a huge data set.

In the code below the straight memcpy is only my latest attempt at getting the input data in the cor开发者_运维问答rect format, I spent a while using the built in OpenCV functions, but this is difficult when your base type is CV_32FC(49).

Can OpenCV 1.1's KMeans algorithm support this sort of high dimensional analysis?

Does someone know the correct method of copying from images to the K-Means input matrix?

Can someone point me to a free, Non-GPL KMeans algorithm I can use instead?

This isn't the best code as I am just trying to get things to work right now:

    std::vector<int> DoKMeans(std::vector<IplImage *>& chunks){
 // the size of one image patch, CELL_SIZE = 7
 int chunk_size = CELL_SIZE*CELL_SIZE*sizeof(float);
 // create the input data, CV_32FC(49) is 7x7 float object (I think)
 CvMat* data = cvCreateMat(chunks.size(),1,CV_32FC(49) );


 // Create a temporary vector to hold our data
 // we'll copy into the matrix for KMeans
 int rdsize = chunks.size()*CELL_SIZE*CELL_SIZE;
 float * rawdata = new float[rdsize];

 // Go through each image chunk and copy the 
 // pixel values into the raw data array.
 vector<IplImage*>::iterator iter;
 int k = 0;
 for( iter = chunks.begin(); iter != chunks.end(); ++iter )
 {

  for( int i =0; i < CELL_SIZE; i++)
  {
   for( int j=0; j < CELL_SIZE; j++)
   {
    CvScalar val;
    val = cvGet2D(*iter,i,j);
    rawdata[k] = (float)val.val[0];
    k++;
   }

  }
 }

 // Copy the data into the CvMat for KMeans
 // I have tried various methods, but this is just the latest.
 memcpy( data->data.ptr,rawdata,rdsize*sizeof(float));

 // Create the output array
 CvMat* results = cvCreateMat(chunks.size(),1,CV_32SC1);

 // Do KMeans
 int r = cvKMeans2(data, 128,results, cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 1000, 0.1));

 // Copy the grouping information to our output vector
 vector<int> retVal;
 for( int y = 0; y < chunks.size(); y++ )
 {
  CvScalar cvs = cvGet1D(results, y);
  int g =  (int)cvs.val[0];
  retVal.push_back(g);
 }

 return retVal;}

Thanks in advance!

Though I'm not familiar with "bag of features", have you considered using feature points like corner detectors and SIFT?

You might like to check out http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/ for another open source clustering package.

Using memcpy like this seems suspect, because when you do:

 int rdsize = chunks.size()*CELL_SIZE*CELL_SIZE;

If CELL_SIZE and chunks.size() are very large you are creating something large in rdsize. If this is bigger than the largest storable integer you may have a problem.

Are you wanting to change "chunks" in this function? I'm guessing that you don't as this is a K-means problem.

So try passing by reference to const here. (And generally speaking this is what you will want to be doing)

so instead of:

std::vector<int> DoKMeans(std::vector<IplImage *>& chunks)

it would be:

std::vector<int> DoKMeans(const std::vector<IplImage *>& chunks)

Also in this case it is better to use static_cast than the old c style casts. (for example static_cast(variable) as opposed to (float)variable ).

Also you may want to delete "rawdata":

 float * rawdata = new float[rdsize];

can be deleted with:

delete[] rawdata;

otherwise you may be leaking memory here.