using Gabor wavelet to extract texture features

Hello,

I'm trying to build a code for extracting texture features by gabor wavelet, also after searching the net and experimenting with what i found, I do some changes but i'm not sure if it is correct, in other words, if these changes will give me what I want which is to use standard deviation and mean for a feature vector .Please note that Gabor channels are (6 orientation and 5 scales)

my images have the size of 348 by 256 and I used m = n = 40 , d1 =77 , d2 = 43 .

My question is how should I calculate d1 and d2 for downsampling and upsampling and is the code correct for extracting mean and standard deviation.

% to create Gabor Array 
function gaborArray = gaborFilterBank(u,v,m,n)

% GABORFILTERBANK generates a custum Gabor filter bank. 
% It creates a u by v cell array, whose elements are m by n matrices; 
% each matrix being a 2-D Gabor filter.
% 
% 
% Inputs:
%       u	:	No. of scales (usually set to 5) 
%       v	:	No. of orientations (usually set to 6)
%       m	:	No. of rows in a 2-D Gabor filter (an odd integer number, usually set to 39)
%       n	:	No. of columns in a 2-D Gabor filter (an odd integer number, usually set to 39)
% 
% Output:
%       gaborArray: A u by v array, element of which are m by n 
%                   matries; each matrix being a 2-D Gabor filter   
% 
% 
% Sample use:
% 
% gaborArray = gaborFilterBank(5,6,40,40);
% 
% 
% 
%   Details can be found in:
%   
%   M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, "CloudID: Trustworthy 
%   cloud-based and cross-enterprise biometric identification," 
%   Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015.
% 
% 
% 
% (C)	Mohammad Haghighat, University of Miami
%       [e-mail]
%       PLEASE CITE THE ABOVE PAPER IF YOU USE THIS CODE.



if (nargin ~= 4)    % Check correct number of arguments
    error('There must be four input arguments (Number of scales and orientations and the 2-D size of the filter)!')
end


%% Create Gabor filters
% Create u*v gabor filters each being an m by n matrix

gaborArray = cell(u,v);
fmax = 0.25;
gama = sqrt(2);
eta = sqrt(2);

for i = 1:u
    
    fu = fmax/((sqrt(2))^(i-1));
    alpha = fu/gama;
    beta = fu/eta;
    
    for j = 1:v
        tetav = ((j-1)/v)*pi;
        gFilter = zeros(m,n);
        
        for x = 1:m
            for y = 1:n
                xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);
                yprime = -(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav);
                gFilter(x,y) = (fu^2/(pi*gama*eta))*exp(-((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);
            end
        end
        gaborArray{i,j} = gFilter;
        
    end
end





function [featureVector,gaborResult] = gaborFeatures1(img,d1,d2)
%% Filter the image using the Gabor filter bank

% Filter input image by each Gabor filter
load 'gaborArray.mat';
[u,v] = size(gaborArray);
gaborResult = cell(u,v);
for i = 1:u
    for j = 1:v
        gaborResult{i,j} = imfilter(img, gaborArray{i,j});
    end
end

%% Create feature vector

% Extract feature vector from input image
featureVector1 = [];
featureVector1 = [];
gaborResult = gaborResult(:);
gaborResult = gaborResult' ;

for i = 1:30
    gaborResultt = cell2mat (gaborResult(i));
    MeanValue = mean2 (gaborResultt);
     MeanValue = abs( MeanValue);
    
    STDValue  = std2 (gaborResultt);
    
    STDValue = abs(STDValue);
   featureVector1(i) =  MeanValue  ;
featureVector2(i) =  STDValue   ;     
end 
featureVector = [ featureVector1 ;featureVector2 ];
featureVector = featureVector(:);
featureVector = featureVector';
end 

[KALYAN ACHARJYA]

If you want help, you need to make it easy to help. Please ensure the following-

• What do you have?
• Desirable Results?

Additional non-important things disturb to understand the question.

[Ibtihaal Hameed]

You are right. What I want is to extract texture features using Gabor filter the final feature vector should be 1by 60 and represented as :

f=(μ_00,σ_(00) μ_11,σ_(11),………..,μ_30,σ_(30))

where μ is the mean value and σ is the standard deviation