SVM.zip_SVM_happenldn_matlab svm

function [trainingFeatures,trainingLabels,testFeatures,testLabels]=extractFeature(trainingSet,testSet) %% 确定特征向量尺寸 img = read(trainingSet(1), 1); %转化为灰度图像 img=rgb2gray(img); %转化为2值图像 lvl = graythresh(img); img = im2bw(img, lvl); img=imresize(img,[256 256]); cellSize = [4 4]; [hog_feature, vis_hog] = extractHOGFeatures(img,'CellSize',cellSize); glcm_feature = getGLCMFeatures(img); SizeOfFeature = length(hog_feature)+ length(glcm_feature); %% 构建训练样本特征向量和训练样本标签 trainingFeatures = []; trainingLabels = []; for digit = 1:numel(trainingSet) numImages = trainingSet(digit).Count; features = zeros(numImages, SizeOfFeature, 'single');%初始化特征向量 % 遍历每张图片 for i = 1:numImages img = read(trainingSet(digit), i);% 取出第i张图片 img=rgb2gray(img); % 转化为灰度图像 glcm_feature = getGLCMFeatures(img); % 提取GLCM特征 lvl = graythresh(img); % 阈值化 img = im2bw(img, lvl); % 转化为2值图像 img=imresize(img,[256 256]); % 提取HOG特征 [hog_feature, vis_hog] = extractHOGFeatures(img,'CellSize',cellSize); % 合并两个特征 features(i, :) = [hog_feature glcm_feature]; end % 使用图像描述作为训练标签 labels = repmat(trainingSet(digit).Description, numImages, 1); % 逐个添加每张训练图片的特征和标签 trainingFeatures = [trainingFeatures; features]; trainingLabels = [trainingLabels; labels]; end %% 提取测试图片集的特征向量 testFeatures = []; testLabels = []; for digit = 1:numel(testSet) numImages = testSet(digit).Count; %初始化特征向量 features = zeros(numImages, SizeOfFeature, 'single'); for i = 1:numImages img = read(testSet(digit), i); %转化为灰度图像 img=rgb2gray(img); glcm_feature = getGLCMFeatures(img); %转化为2值图像 lvl = graythresh(img); img = im2bw(img, lvl); img=imresize(img,[256 256]); [hog_4x4, vis4x4] = extractHOGFeatures(img,'CellSize',cellSize); features(i, :) = [hog_4x4 glcm_feature]; end % 使用图像描述作为训练标签 labels = repmat(testSet(digit).Description, numImages, 1); testFeatures = [testFeatures; features]; testLabels=[testLabels; labels]; end end