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基于MATLAB的HOG+GLCM特征提取与SVM分类实现

news 2026/3/27 0:04:19

一、核心流程

数据准备：组织训练集/测试集文件夹结构
图像预处理：灰度化、归一化、尺寸统一
特征提取：HOG（梯度方向直方图） + GLCM（灰度共生矩阵）
特征融合：串联HOG与GLCM特征向量
SVM分类：训练多类分类模型并评估性能

二、MATLAB代码实现

1. 数据预处理与特征提取

%% 参数设置
imgSize = [64,64];      % 统一图像尺寸
cellSize = [8,8];       % HOG单元尺寸
distances = [1];        % GLCM计算距离
angles = [0, pi/4, pi/2, 3*pi/4]; % GLCM方向%% 加载数据集
trainingSet = imageSet('train_data/', 'recursive');
testSet = imageSet('test_data/', 'recursive');%% 特征提取函数
function features = extractFeatures(img)% 灰度化与尺寸调整grayImg = rgb2gray(imresize(img, imgSize));% 提取HOG特征hogFeat = extractHOGFeatures(grayImg, 'CellSize', cellSize);% 提取GLCM特征glcmFeat = [];for i = 1:length(angles)glcm = graycomatrix(grayImg, 'Offset', [0, distances(i)*sin(angles(i)), 0, distances(i)*cos(angles(i))]);glcmFeat = [glcmFeat, graycoprops(glcm, {'Contrast', 'Energy', 'Correlation', 'Homogeneity'})];end% 特征融合features = [hogFeat, glcmFeat];
end%% 构建特征矩阵与标签
trainingFeatures = [];
trainingLabels = [];
testFeatures = [];
testLabels = [];for i = 1:trainingSet.Countimg = read(trainingSet, i);label = trainingSet(i).Description;features = extractFeatures(img);trainingFeatures = [trainingFeatures; features];trainingLabels = [trainingLabels; repmat(label, size(features,1), 1)];
endfor i = 1:testSet.Countimg = read(testSet, i);label = testSet(i).Description;features = extractFeatures(img);testFeatures = [testFeatures; features];testLabels = [testLabels; repmat(label, size(features,1), 1)];
end

2. SVM分类模型训练

%% 数据标准化
scaler = fitcecoc(trainingFeatures, trainingLabels, 'Learners', 'svm', ...'Coding', 'onevsall', 'CrossVal', 'on', 'KFold', 5);%% 模型训练
svmModel = fitcecoc(trainingFeatures, trainingLabels, ...'Learners', templateSVM('KernelFunction', 'rbf', 'BoxConstraint', 10, 'KernelScale', 'auto'));%% 预测与评估
predictedLabels = predict(svmModel, testFeatures);
confMat = confusionmat(testLabels, predictedLabels);
accuracy = sum(diag(confMat))/sum(confMat(:));
disp(['分类准确率: ', num2str(accuracy*100, '%.2f'), '%']);

三、关键优化

1. 特征降维

% 使用PCA降维（保留95%方差）
[coeff, score, ~] = pca(trainingFeatures);
selectedFeatures = score(:,1:100); % 选择前100个主成分

2. 参数调优

% 网格搜索优化SVM参数
tuneModel = fitcsvm(trainingFeatures, trainingLabels, ...'KernelFunction', 'rbf', ...'OptimizeHyperparameters', 'auto', ...'HyperparameterOptimizationOptions', struct('AcquisitionFunctionName', 'expected-improvement-plus'));

3. 类别不平衡处理

% 添加类别权重
classWeights = [0.8, 0.2]; % 根据样本比例设置
svmModel = fitcecoc(trainingFeatures, trainingLabels, ...'ClassNames', unique(trainingLabels), ...'Cost', [0 1; 2 0], ... % 错误分类惩罚矩阵'ClassNames', classWeights);

四、性能评估

%% 分类报告
disp('分类报告:');
disp(classificationReport(testLabels, predictedLabels));%% ROC曲线（二分类示例）
[X,Y,T,AUC] = perfcurve(testLabels, predictedLabels, 'cat1');
figure;
plot(X,Y);
xlabel('False Positive Rate');
ylabel('True Positive Rate');
title(['AUC = ', num2str(AUC, '%.2f')]);

参考代码对图片提取HOG、GLCM特征，利用SVM进行分类 www.youwenfan.com/contentcnq/60101.html

五、完整代码结构

Project/
├── data/
│   ├── train_data/  # 训练集（子文件夹为类别）
│   └── test_data/   # 测试集
├── src/
│   ├── feature_extraction.m  # 特征提取函数
│   └── svm_classifier.m      # SVM分类函数
├── results/
│   ├── confusion_matrix.png
│   └── roc_curve.png
└── main.m                    # 主程序入口