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I've read of VLFeat, which has an implementation of the algorithm of k-mean, which is faster than that of OpenCV, but do not understand how to readjust this code to use VLFeat.

Mat rawFeaturesData; // mat with images dataset features  
...
Mat labels, centers;
int bin_size = 1000;
kmeans(rawFeatureData, bin_size, labels, TermCriteria( TermCriteria::COUNT+TermCriteria::EPS, 100, 1.0),
       3, KMEANS_PP_CENTERS, centers);

Could someone tell me how to do? Thank you.

I've read of VLFeat, which has an implementation of the algorithm of k-mean, which is faster than that of OpenCV, but do not understand how to readjust this code to use VLFeat.

Mat rawFeaturesData; // mat with images dataset features  
...
Mat labels, centers;
int bin_size = 1000;
kmeans(rawFeatureData, bin_size, labels, TermCriteria( TermCriteria::COUNT+TermCriteria::EPS, 100, 1.0),
       3, KMEANS_PP_CENTERS, centers);

Could someone tell me how to do? Thank you.

I've read of VLFeat, which has an implementation of the algorithm of k-mean, which is faster than that of OpenCV, but do not understand how to readjust this code (in c++ and OpenCV) to use VLFeat.

Mat rawFeaturesData; // mat with images dataset features  
...
Mat labels, centers;
int bin_size = 1000;
kmeans(rawFeatureData, bin_size, labels, TermCriteria( TermCriteria::COUNT+TermCriteria::EPS, 100, 1.0),
       3, KMEANS_PP_CENTERS, centers);

Could someone tell me how to do? Thank you.