Hi
I have this matrix A, representing similarities of pixel intensities of an image. For example: Consider a 10 x 10 image. Matrix A in this case would be of dimension 100 x 100, and element A(i,j) would have a value in the range 0 to 1, representing the similarity of pixel i to j in terms of intensity.
I am using OpenCV for image processing and the development environment is C on Linux.
Objective is to compute the Eigenvectors of matrix A and I have used the following approach:
static CvMat mat, *eigenVec, *eigenVal;
static double A[100][100]={}, Ain1D[10000]={};
int cnt=0;
//Converting matrix A into a one dimensional array
//Reason: That is how cvMat requires it
for(i = 0;i < affnDim;i++){
for(j = 0;j < affnDim;j++){
Ain1D[cnt++] = A[i][j];
}
}
mat = cvMat(100, 100, CV_32FC1, Ain1D);
cvEigenVV(&mat, eigenVec, eigenVal, 1e-300);
for(i=0;i < 100;i++){
val1 = cvmGet(eigenVal,i,0); //Fetching Eigen Value
for(j=0;j < 100;j++){
matX[i][j] = cvmGet(eigenVec,i,j); //Fetching each component of Eigenvector i
}
}
Problem: After execution I get nearly all components of all the Eigenvectors to be zero. I tried different images and also tried populating A with random values between 0 and 1, but the same result.
Few of the top eigenvalues returned look like the following:
9805401476911479666115491135488.000000
-9805401476911479666115491135488.000000
-89222871725331592641813413888.000000
89222862280598626902522986496.000000
5255391142666987110400.000000
I am now thinking on the lines of using cvSVD() which performs singular value decomposition of real floating-point matrix and might yield me the eigenvectors. But before that I thought of asking it here. Is there anything absurd in my current approach? Am I using the right API i.e. cvEigenVV() for the right input matrix (my matrix A is a floating point matrix)?
cheers
