How can i save a single .mat file of features for many images (loop for)?

i trained two-class-classification (svmtrain) with 15 features for 18 images and i have different number of objects in a single image. I want to save single .mat file of features of these images?my problem is when i run this code i have just the variables of last image!!This is my code :

Answer 1

[KSSV]

You can save all the data into a single .mat file.....with different variable names.

[Braiki Marwa]

Hi thanks for your response but my problem is when i run this code i have just the variables of last image!!

for n=n1:n2 (number of images)
for k=1:length(B3) (number of objects in a signle image)

Feat(k,:)=[Area_cellule,Perimeter_cellule,Circularity_cellule,Compactness_cellule, Solidity_cellule,Eccentricity_cellule,MajorAxis_cellule,MinorAxis_cellule, Centroid_cellule,Ampl,Contrast_Cellule,Correlation_Cellule,Energy_Cellule,Homogeneity_Cellule];

     end
  end
  save('FeatTrain.mat', 'Feat');

[KSSV]

Feat is a matrix...you should be having all of it?

[Stephen23]

"with different variable names."

Better would be to use one array, e.g. a cell array or an ND numeric array.

[Braiki Marwa]

This is my code :

NbIm = size(names1,1);

n1 = 1;

n2 = NbIm;

for n=n1:n2

%1- Read the original image

%2- Processing : Segmentation

%3- characterization :

[B3,L3,N3] = bwboundaries(ICellules); (see picture)

CC = bwconncomp(L3);

BW=bwlabel(L3); stats1=regionprops(CC,'Area','Centroid','Eccentricity','Perimeter','ConvexArea','ConvexHull','ConvexImage','MajorAxisLength','MinorAxisLength','Orientation','Solidity','BoundingBox');

for k=1:length(B3),

V=[];glcm=[];

V=Im_originale(BW==k);

glcm = graycomatrix(V,'Offset',[2 0],'Symmetric', true);

stats= graycoprops(glcm);

Contrast_Cellule =stats.Contrast;

 Correlation_Cellule =stats.Correlation;

 Energy_Cellule =stats.Energy;

Homogeneity_Cellule =stats.Homogeneity;

 Area_cellule = stats1(k).Area;

Perimeter_cellule = stats1(k).Perimeter;

Circularity_cellule= (4*pi*Area_cellule)/Perimeter_cellule^2;

Centroid_cellule = stats1(k).Centroid;

Compactness_cellule=Perimeter_cellule^2/(4*pi*Area_cellule);

MajorAxis_cellule=stats1(k). MajorAxisLength;

MinorAxis_cellule=stats1(k). MinorAxisLength;

Orientation_cellule =stats1(k).Orientation;

Eccentricity_cellule=stats1(k).Eccentricity;

   Solidity_cellule=stats1(k).Solidity;

    boundary3 = B3{k};

    [cc] = chaincode(boundary3);

    ai=cc.code;

    ai = ai.';

    output = calc_harmonic_coefficients(ai,30);

    Ampl=0.5*sqrt((output(1)^2)+(output(2)^2)+(output(3)^2)+(output(4)^2));
Feat(k,:)=[Area_cellule,Perimeter_cellule,Circularity_cellule,Compactness_cellule, Solidity_cellule,Eccentricity_cellule,MajorAxis_cellule,MinorAxis_cellule, Centroid_cellule,Ampl,Contrast_Cellule,Correlation_Cellule,Energy_Cellule,Homogeneity_Cellule];

end

end

save('FeatTrain.mat', 'Feat');