url = 'http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz';
outputFolder = fullfile(tempdir, 'caltech101');
if ~exist(outputFolder, 'dir')
disp('Downloading 126MB Caltech101 data set...');
untar(url, outputFolder);
end
rootFolder = fullfile(outputFolder, '101_ObjectCategories');
categories = {'airplanes', 'ferry', 'laptop'};
imds = imageDatastore(fullfile(rootFolder, categories), 'LabelSource', 'foldernames');
[trainingSet, validationSet] = splitEachLabel(imds, 0.3, 'randomize');
bag = bagOfFeatures(trainingSet);
trainFeatures = encode(bag, trainingSet);
SVM_SURF = fitcecoc(trainFeatures,trainingSet.Labels);
featureMatrix = encode(bag, validationSet);
[pred score cost] = predict(SVM_SURF, featureMatrix)
accuracy = sum(validationSet.Labels == pred)/size(validationSet.Labels,1);
accuracy
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