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svm_glcm_colormoments.m
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svm_glcm_colormoments.m
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RootTrainPath = 'F:\SKRIPSI 2019-2020\6. Giovanni Tjahyamulia - 1620250081\image_train\';
RootTestPath = 'F:\SKRIPSI 2019-2020\6. Giovanni Tjahyamulia - 1620250081\image_test\';
RootTrain = dir(RootTrainPath);
RootTest = dir(RootTestPath);
X_train = [];
Y_train = [];
index_file = 0;
index_folder = 0;
for i = 1:length(RootTrain)
FolderName = RootTrain(i).name;
if(FolderName ~= '.' | FolderName ~= '..')
% disp(strcat('Extracting feature from ', FolderName));
index_folder = index_folder + 1;
FolderPath = strcat(RootTrainPath, FolderName, "\");
File = dir(FolderPath);
for j = 1:length(File)
FileName = File(j).name;
if(FileName == '.')
else
index_file = index_file + 1;
FilePath = strcat(FolderPath, FileName);
disp(FilePath);
image = imread(FilePath);
% extract color channels
R = double(image(:, :, 1));
G = double(image(:, :, 2));
B = double(image(:, :, 3));
meanR = mean(R(:));
meanG = mean(G(:));
meanB = mean(B(:));
varianceR = std(R(:));
varianceG = std(G(:));
varianceB = std(B(:));
differenceR = double(0);
differenceG = double(0);
differenceB = double(0);
for row = 1:length(image)
for col = 1:length(image(1))
differenceR = differenceR + double(image(row, col, 1) - meanR);
differenceG = differenceG + double(image(row, col, 2) - meanG);
differenceB = differenceB + double(image(row, col, 3) - meanB);
end
end
N = length(image) * length(image(1));
skewnessR = nthroot(((differenceR^3)/N), 3);
skewnessG = nthroot(((differenceG^3)/N), 3);
skewnessB = nthroot(((differenceB^3)/N), 3);
glcm_stats = graycoprops(rgb2gray(image));
X_train(index_file, 1) = glcm_stats.Contrast;
X_train(index_file, 2) = glcm_stats.Correlation;
X_train(index_file, 3) = glcm_stats.Energy;
X_train(index_file, 4) = glcm_stats.Homogeneity;
X_train(index_file, 5) = meanR;
X_train(index_file, 6) = meanG;
X_train(index_file, 7) = meanB;
X_train(index_file, 8) = varianceR;
X_train(index_file, 9) = varianceG;
X_train(index_file, 10) = varianceB;
X_train(index_file, 11) = skewnessR;
X_train(index_file, 12) = skewnessG;
X_train(index_file, 13) = skewnessB;
Y_train(index_file) = index_folder;
end
end
end
end
X_test = [];
Y_test = [];
index_file = 0;
index_folder = 0;
file_test_name = [];
for i = 1:length(RootTest)
FolderName = RootTest(i).name;
if(FolderName ~= '.' | FolderName ~= '..')
% disp(strcat('Extracting feature from ', FolderName));
index_folder = index_folder + 1;
FolderPath = strcat(RootTestPath, FolderName, "\");
File = dir(FolderPath);
for j = 1:length(File)
FileName = File(j).name;
if(FileName == '.')
else
index_file = index_file + 1;
FilePath = strcat(FolderPath, FileName);
image = imread(FilePath);
% extract color channels
R = double(image(:, :, 1));
G = double(image(:, :, 2));
B = double(image(:, :, 3));
meanR = mean(R(:));
meanG = mean(G(:));
meanB = mean(B(:));
varianceR = std(R(:));
varianceG = std(G(:));
varianceB = std(B(:));
differenceR = double(0);
differenceG = double(0);
differenceB = double(0);
for row = 1:length(image)
for col = 1:length(image(1))
differenceR = differenceR + double(image(row, col, 1) - meanR);
differenceG = differenceG + double(image(row, col, 2) - meanG);
differenceB = differenceB + double(image(row, col, 3) - meanB);
end
end
N = length(image) * length(image(1));
skewnessR = nthroot(((differenceR^3)/N), 3);
skewnessG = nthroot(((differenceG^3)/N), 3);
skewnessB = nthroot(((differenceB^3)/N), 3);
glcm_stats = graycoprops(rgb2gray(image));
X_test(index_file, 1) = glcm_stats.Contrast;
X_test(index_file, 2) = glcm_stats.Correlation;
X_test(index_file, 3) = glcm_stats.Energy;
X_test(index_file, 4) = glcm_stats.Homogeneity;
X_test(index_file, 5) = meanR;
X_test(index_file, 6) = meanG;
X_test(index_file, 7) = meanB;
X_test(index_file, 8) = varianceR;
X_test(index_file, 9) = varianceG;
X_test(index_file, 10) = varianceB;
X_test(index_file, 11) = skewnessR;
X_test(index_file, 12) = skewnessG;
X_test(index_file, 13) = skewnessB;
Y_test(index_file) = index_folder;
end
end
end
end
save('SVM-GLCM-ColorMoments_TrainFeatures.mat', 'X_train')
save('SVM-GLCM-ColorMoments_TrainLabels.mat', 'Y_train')
save('SVM-GLCM-ColorMoments_TestFeatures.mat', 'X_test')
save('SVM-GLCM-ColorMoments_TestLabels.mat', 'Y_test')
SVMModel = fitcecoc(X_train, Y_train);
Y_predict = predict(SVMModel, X_test);
ConfusionMatrix = confusionmat(Y_test, Y_predict);
confusionchart(ConfusionMatrix)
save('SVM-GLCM-ColorMoments_ConfusionMatrix.mat', 'ConfusionMatrix');