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This is a silly question since I'm quite new to SVM,

I've managed to extract features and locations using:

vector< float > features;
vector< Point > locations;
hog_descriptors.compute( image, features, Size(0, 0), Size(0, 0), locations );

Then I proceed to use CvSVM to train the SVM based on the features I've extracted.

Mat training_data( features );
CvSVM svm;
svm.train( training_data, labels, Mat(), Mat(), params );

Which gave me an error:

OpenCV Error: Sizes of input arguments do not match (Response array must contain as many elements as the total number of samples)

My question is that, how do I convert the vector < features > into appropriate matrix to be fed into CvSVM ? Obviously I am doing something wrong, the given tutorial shows that a 2D matrix containing the training data is fed into SVM. So, how do I convert vector < features > into a 2D matrix, what are the values in the 2nd dimension ?

Could anyone please explain to me how am I supposed to tackle this ?

This is a silly question since I'm quite new to SVM,

I've managed to extract features and locations using:

vector< float > features;
vector< Point > locations;
hog_descriptors.compute( image, features, Size(0, 0), Size(0, 0), locations );

Then I proceed to use CvSVM to train the SVM based on the features I've extracted.

Mat training_data( features );
CvSVM svm;
svm.train( training_data, labels, Mat(), Mat(), params );

Which gave me an error:

OpenCV Error: Sizes of input arguments do not match (Response array must contain as many elements as the total number of samples)
Bad argument (There is only a single class) in cvPreprocessCategoricalResponses, file /opt/local/var/macports/build/

My question is that, how do I convert the vector < features > into appropriate matrix to be fed into CvSVM ? Obviously I am doing something wrong, the given tutorial shows that a 2D matrix containing the training data is fed into SVM. So, how do I convert vector < features > into a 2D matrix, what are the values in the 2nd dimension ?

Could anyone please explain to me how am I supposed to tackle this ?