利用matlab仿真实现基于图像HOG特征和SVM实现车型识别的代码

以下是一个基于Matlab的车型识别代码示例，其中使用了HOG特征和SVM分类器：

1. 数据集准备：

% 导入车辆数据集
vehicleDataset = imageDatastore('vehicles', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

% 随机划分训练集和测试集
rng(0);
[trainDataset, testDataset] = splitEachLabel(vehicleDataset, 0.8, 'randomize');

2. 特征提取：

% 定义HOG特征提取器
cellSize = [4 4];
hogFeatureSize = 144;
hogFeatureExtractor = @helperExtractHOGFeatures;

% 提取训练集和测试集的HOG特征
trainFeatures = helperExtractFeatures(trainDataset, hogFeatureExtractor, cellSize, hogFeatureSize);
testFeatures = helperExtractFeatures(testDataset, hogFeatureExtractor, cellSize, hogFeatureSize);

3. 特征选择：

% 使用PCA方法对训练集的特征进行降维
numFeatures = size(trainFeatures, 2);
[coeff, score, ~, ~, explained] = pca(trainFeatures);
numComponents = find(cumsum(explained) >= 95, 1);
trainFeatures = score(:, 1:numComponents);
testFeatures = bsxfun(@minus, testFeatures, mean(trainFeatures, 1)) * coeff(:, 1:numComponents);

4. 训练分类器：

% 训练SVM分类器
SVMModel = fitcsvm(trainFeatures, trainDataset.Labels, 'KernelFunction', 'rbf', 'Standardize', true, 'ClassNames', unique(trainDataset.Labels));

5. 测试分类器：

% 使用测试集评估分类器的性能
predictedLabels = predict(SVMModel, testFeatures);
accuracy = mean(predictedLabels == testDataset.Labels);

6. 应用场景：

% 加载测试图像并进行车型识别
testImage = imread('test.jpg');
testImage = imresize(testImage, [64 64]);
testFeatures = helperExtractHOGFeatures(testImage, cellSize, hogFeatureSize);
testFeatures = bsxfun(@minus, testFeatures, mean(trainFeatures, 1)) * coeff(:, 1:numComponents);
predictedLabel = predict(SVMModel, testFeatures);

其中，`helperExtractHOGFeatures`和`helperExtractFeatures`是自定义函数，用于提取HOG特征和特征向量。

这是一个简单的车型识别实现示例，具体的实现方法和代码结构可能因应用场景和需求而异。