基于纹理的图像检索C++

基于纹理的图像检索是一种常用的图像检索方法，它通过提取图像的纹理特征来实现图像的相似性比较和检索。在C++中，可以使用OpenCV库来实现基于纹理的图像检索。

下面是一个基于纹理的图像检索的C++示例代码：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

// 计算图像的纹理特征
Mat calculateTextureFeatures(Mat image) {
    // 将图像转换为灰度图像
    Mat grayImage;
    cvtColor(image, grayImage, COLOR_BGR2GRAY);

    // 定义Gabor滤波器参数
    int numScales = 4;  // 尺度数
    int numOrientations = 6;  // 方向数

    // 创建Gabor滤波器
    vector<Mat> gaborFilters;
    for (int scale = 0; scale < numScales; scale++) {
        for (int orientation = 0; orientation < numOrientations; orientation++) {
            double theta = orientation * CV_PI / numOrientations;
            double sigma = pow(2.0, scale) * 2.0;
            double lambda = CV_PI / 2.0;
            double gamma = 0.5;

            Mat gaborFilter = getGaborKernel(Size(7, 7), sigma, theta, lambda, gamma);
            gaborFilters.push_back(gaborFilter);
        }
    }

    // 对图像进行滤波
    Mat textureFeatures;
    for (int i = 0; i < gaborFilters.size(); i++) {
        Mat filteredImage;
        filter2D(grayImage, filteredImage, CV_32F, gaborFilters[i]);
        textureFeatures.push_back(filteredImage);
    }

    // 归一化纹理特征
    normalize(textureFeatures, textureFeatures, 0, 255, NORM_MINMAX, CV_8U);

    return textureFeatures;
}

// 图像检索
void imageRetrieval(Mat queryImage, vector<Mat> databaseImages) {
    // 计算查询图像的纹理特征
    Mat queryFeatures = calculateTextureFeatures(queryImage);

    // 计算数据库图像的纹理特征
    vector<Mat> databaseFeatures;
    for (int i = 0; i < databaseImages.size(); i++) {
        Mat databaseFeatures = calculateTextureFeatures(databaseImages[i]);
        databaseFeatures.push_back(databaseFeatures);
    }

    // 计算查询图像与数据库图像的相似性
    vector<double> similarities;
    for (int i = 0; i < databaseFeatures.size(); i++) {
        double similarity = compareHist(queryFeatures, databaseFeatures[i], HISTCMP_CORREL);
        similarities.push_back(similarity);
    }

    // 根据相似性排序数据库图像
    vector<int> sortedIndices;
    for (int i = 0; i < similarities.size(); i++) {
        sortedIndices.push_back(i);
    }
    sort(sortedIndices.begin(), sortedIndices.end(), [&](int a, int b) {
        return similarities[a] > similarities[b];
    });

    // 输出相似性最高的图像
    for (int i = 0; i < sortedIndices.size(); i++) {
        int index = sortedIndices[i];
        cout << "Similarity with database image " << index << ": " << similarities[index] << endl;
        imshow("Database Image " + to_string(index), databaseImages[index]);
    }

    waitKey(0);
}

int main() {
    // 读取查询图像
    Mat queryImage = imread("query_image.jpg");

    // 读取数据库图像
    vector<Mat> databaseImages;
    databaseImages.push_back(imread("database_image1.jpg"));
    databaseImages.push_back(imread("database_image2.jpg"));
    databaseImages.push_back(imread("database_image3.jpg"));

    // 进行图像检索
    imageRetrieval(queryImage, databaseImages);

    return 0;
}

这段代码演示了如何使用OpenCV库实现基于纹理的图像检索。首先，通过Gabor滤波器提取图像的纹理特征，然后计算查询图像与数据库图像的相似性，并根据相似性对数据库图像进行排序，最后输出相似性最高的图像。