Image retrieval is a fundamental task in computer vision and has various applications, such as image search, object recognition, and video indexing. Content-based image retrieval (CBIR) is an image retrieval technique that aims to find similar images based on their visual content.

In this blog post, we will explore how to implement a content-based image retrieval system using C++. We will cover the key steps involved in CBIR and provide example code to demonstrate the process.

Table of Contents

• Introduction to CBIR
• Feature Extraction
• Feature Matching
• Image Search
• Conclusion

Introduction to CBIR

Content-based image retrieval involves extracting meaningful features from images and using these features to compare and match images. The key idea is to represent images in a high-dimensional feature space, where the distance between feature vectors corresponds to the similarity between images.

Feature Extraction

The first step in CBIR is to extract representative features from the images. There are various feature extraction techniques available, such as color histograms, texture descriptors, and deep learning-based features. These techniques capture different aspects of the image content and provide a compact representation.

Let’s consider an example of extracting color histograms using the OpenCV library in C++:

#include <opencv2/opencv.hpp>

cv::Mat extractColorHistogram(cv::Mat image) {
    cv::Mat hist;
    int bins = 256;
    int channels[] = {0, 1, 2};
    int histSize[] = {bins, bins, bins};
    float range[] = {0, 256};
    const float* ranges[] = {range, range, range};

    cv::calcHist(&image, 1, channels, cv::Mat(), hist, 3, histSize, ranges);

    return hist;
}

Feature Matching

Once the features are extracted, the next step is to compare and match the feature vectors of different images. Different distance metrics, such as Euclidean distance or cosine similarity, can be used to measure the similarity between feature vectors.

Here’s an example of computing the Euclidean distance between two feature vectors:

#include <cmath>
#include <vector>

double computeEuclideanDistance(const std::vector<double>& v1, const std::vector<double>& v2) {
    double distance = 0.0;

    for (int i = 0; i < v1.size(); i++) {
        distance += std::pow(v1[i] - v2[i], 2);
    }

    return std::sqrt(distance);
}

Image Search

Finally, we can perform image search based on the extracted features and matching results. Given a query image, we compare its features with the features of the images in the database and rank them based on their similarity scores.

Here’s a simplified example of performing image search using color histogram features:

#include <iostream>
#include <vector>

void searchImages(const cv::Mat& queryImage, const std::vector<cv::Mat>& database) {
    cv::Mat queryFeatures = extractColorHistogram(queryImage);

    for (const auto& image : database) {
        cv::Mat imageFeatures = extractColorHistogram(image);
        double similarity = computeEuclideanDistance(queryFeatures, imageFeatures);

        std::cout << "Similarity with image " << image.name << ": " << similarity << std::endl;
    }
}

Conclusion

In this blog post, we discussed the basics of content-based image retrieval (CBIR) and demonstrated how to implement a CBIR system using C++. We covered feature extraction, feature matching, and image search steps, providing example code for each stage.

CBIR is a vast topic with many advanced techniques and variations. By understanding the core concepts and implementing a simple system, you can explore and build more sophisticated CBIR applications using C++.

#CBIR #C++