Calculates the covariance matrix of a set of vectors.

[covar,mean] = cv.calcCovarMatrix(samples)
[...] = cv.calcCovarMatrix(..., 'OptionName', optionValue, ...)

Input

• samples Samples stored as rows/columns of a single matrix.

Output

• covar Output covariance matrix of the type CType and square size.
• mean Output array as the average value of the input vectors.

Options

• Mean Input array as the average value of the input vectors. See 'UseAvg' below. It is not set by default
• Flags Operation flags. Default is equivalent to {'Normal',true, 'Rows',true}
• Normal The covariance matrix will be a square matrix of the same size as the total number of elements in each input vector. One and only one of 'Scrambled' and 'Normal' must be specified. default true
• Scrambled The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used for fast cv.PCA of a set of very large vectors (see, for example, the EigenFaces technique for face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of the "scrambled" covariance matrix. default false
• Rows If the flag is specified, all the input vectors are stored as rows of the samples matrix. mean should be a single-row vector in this case. default true
• Cols If the flag is specified, all the input vectors are stored as columns of the samples matrix. mean should be a single-column vector in this case. default false
• Scale If the flag is specified, the covariance matrix is scaled. In the "normal" mode, scale is 1./nsamples. In the "scrambled" mode, scale is the reciprocal of the total number of elements in each input vector. By default (if the flag is not specified), the covariance matrix is not scaled (scale=1). default false
• UseAvg If the flag is specified, the function does not calculate mean from the input vectors but, instead, uses the passed mean vector. This is useful if mean has been pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In this case, mean is not a mean vector of the input sub-set of vectors but rather the mean vector of the whole set. default false
• CType type of the matrix. default is 'double'

The functions cv.calcCovarMatrix calculate the covariance matrix and, optionally, the mean vector of the set of input vectors.