How to obtain mean and std as Gabor Wavelet Feature - c++

Question

i'm trying to implement Gabor Wavelet feature as described in this paper:
"Texture Features for Browsing and Retrieval of Image Data"
the feature vector is composed from mean and standard deviation (example of feature vector below has scale=4 and orientation=6)
Implementation code:
void gabor_main(int argc, char **argv)
{
int img_height; // height of input image
int img_width; // width of input image
int side; // side (filter dimension = (2*side+1)*(2*side+1)) = 60
int scale; // number of scale
int orientation; // number of orientation
int flag; // flag (removing the DC term) = 0 (False)
FILE* fp;
unsigned char *tmp_raw_img; // temporary raw image data
double Ul; // Uh (highest spatial frequency)
double Uh; // Ul (lowest spatial frequency)
Matrix* img_mat; // input image
Matrix* F_r; // result, real part
Matrix* F_i; // result, imaginary part
Matrix* F_m; // result, magnitude of real part and imaginary part
scale = 4;
orientation = 6;
Ul = 0.1;
Uh = 0.4;
flag = 0;
side = 60;
...
/* ----------------- Reading raw image ----------------- */
...
/* ----------------- Gabor filtered outputs ----------------- */
CreateMatrix(&F_r, img_height * scale, img_width * orientation); // memory allocation of real part matrix of the output
CreateMatrix(&F_i, img_height * scale, img_width * orientation); // memory allocation of imaginary part matrix of the output
CreateMatrix(&F_m, img_height * scale, img_width * orientation); // // memory allocation of magnitude of the output
GaborFilteredImg(F_r, F_i, img_mat, side, Ul, Uh, scale, orientation, flag);
/* ----------------- Compute Feature Vector ----------------- */
// Magnitude of complex value
for (int h = 0; h < (img_height * scale); h++)
{
for (int w = 0; w < (img_width * orientation); w++)
{
F_m->data[h][w] = sqrt(F_r->data[h][w] * F_r->data[h][w] + F_i->data[h][w] * F_i->data[h][w]);
}
}
for(int i = 0; i < scale; i++)
{
for(int j = 0;j < orientation; j++)
{
double avg = Average(F_m, img_height, img_width, i, j);
double std = StandardDeviation(F_m, img_height, img_width, i, j);
// Print the result
std::cout << avg << " " << std << "\n";
}
}
FreeMatrix(F_r);
FreeMatrix(F_i);
FreeMatrix(F_m);
}
code of mean and standard deviation:
double Average(Matrix* F_m, int img_height, int img_width, int scale, int orientation)
{
double avg = 0.0;
for (int h = (img_height * scale); h < (img_height * (scale + 1)); h++)
{
for (int w = (img_width * orientation); w < (img_width * (orientation + 1)); w++)
{
avg += F_m->data[h][w];
}
}
avg /= (img_height * img_width);
return avg;
}
double StandardDeviation(Matrix* F_m, int img_height, int img_width, int scale, int orientation)
{
double std = 0.0;
double avg = Average(F_m, img_height, img_width, scale, orientation);
for (int h = (img_height * scale); h < (img_height * (scale + 1)); h++)
{
for (int w = (img_width * orientation); w < (img_width * (orientation + 1)); w++)
{
double dif = F_m->data[h][w] - avg;
std += (dif * dif);
}
}
std = sqrt(std / (img_height * img_width));
return std;
}
note:
code of function of GaborFilteredImg i copied from this http://vision.ece.ucsb.edu/texture/software/gabor.c
i would like to ask if the code i wrote (starting from "Compute Texture Feature" section) is correct. I am not sure in getting mean and std given output F_r (real part) and F_i(imaginary part). Basically i calculate the mean and std for every response of gabor filter bank
===UPDATE===
Those F_r and F_i are the result of gabor filtering using scale=4 and orientation=6.
Both F_r and F_i have dimension (img_height * scale) * (img_width * orientation) basically are composed of grids for each response of gabor filter bank.
Then i compute the magnitude F_m(x,y) = sqrt(F_r(x, y) * F_r(x, y) + F_i(x, y) * F_i(x, y))
Finally i calculate the feature vector which is the mean and standard deviation of F_m
===IMAGES===
Image input (real): http://goo.gl/kc5BG
Gabor banks (real) : http://goo.gl/0qM4E
Gabor banks (imaginary) : http://goo.gl/r7Fnk
Output (real) : http://goo.gl/nxVMn
Output (imaginary) : http://goo.gl/SnD7p