High-speed image retrieval algorithm and its implementation method in PHP

High-speed image retrieval algorithm and its implementation method in PHP

With the widespread application of digital images, image retrieval technology has also attracted more and more attention. High-speed image retrieval algorithm is an important method in image retrieval, which can quickly find images similar to the query image in massive image data. This article will introduce the high-speed image retrieval algorithm and its implementation method in PHP.

1. Principle of high-speed image retrieval algorithm

The core idea of ​​high-speed image retrieval algorithm is to convert images into feature vectors, and then calculate the similarity between feature vectors to find and query images The image with the highest similarity. Commonly used feature vectors include color histograms, texture features, etc. Among high-speed image retrieval algorithms, the most commonly used ones are color histogram-based algorithms.

Color histogram refers to dividing the color space of the image into several discrete small intervals, counting the number of pixels in each interval, and calling the vector composed of these numbers a color histogram. The color histogram of the query image and the image to be retrieved can be calculated using Euclidean distance or cosine similarity. The calculation formula is as follows:

Euclidean distance: $d(x,y)=sqrt{sum_{i =1}^{n}(x_i-y_i)^2}$

Cosine similarity: $sim(x,y)= rac{x·y}{||x||·||y ||}$

Among them, $x$ and $y$ represent the color histograms of the query image and the image to be retrieved respectively, and $n$ represents the dimension of the color histogram.

2. Implementation of high-speed image retrieval algorithm

In PHP, the high-speed image retrieval algorithm can be implemented with the help of the OpenCV library. OpenCV is an open source computer vision library that can process images and videos and provides a large number of image processing functions and tools. The following takes the high-speed image retrieval algorithm based on color histogram as an example to introduce how to implement it using OpenCV.

1. Preprocessing images

First, the color histograms of all images to be retrieved need to be calculated and saved to the database. The following is a simple code example:

<?php
require 'opencv/opencv.php';

// 连接数据库
$conn = mysqli_connect('localhost', 'root', '', 'image_database');

// 设置OpenCV



// 获取图像列表
$result = mysqli_query($conn, "SELECT * FROM images");

while ($row = mysqli_fetch_assoc($result)) {
    $id = $row['id'];
    $path = $row['path'];

    // 读取图像
    $img = $cv->imread($path);

    // 将图像转换为HSV颜色空间
    $hsv = $cv->cvtColor($img, CV_BGR2HSV);

    // 计算颜色直方图
    $hist = $cv->calcHist(array($hsv), array(0, 1), null, array(180, 256), array(0, 180, 0, 256));

    // 归一化颜色直方图
    $hist = $cv->normalize($hist, 1);

    // 将颜色直方图存入数据库
    $data = $cv->toArray($hist);
    $data = implode(',', $data[0]);
    mysqli_query($conn, "UPDATE images SET hist='$data' WHERE id=$id");
}

// 关闭数据库连接
mysqli_close($conn);
?>

2. Retrieve similar images

Query the color histogram of the image and compare it with the color histogram of all images in the database to calculate the similarity Spend. The following is a simple code example:

<?php
require 'opencv/opencv.php';

// 连接数据库
$conn = mysqli_connect('localhost', 'root', '', 'image_database');

// 设置OpenCV



// 读取查询图像
$query = $cv->imread('query.jpg');

// 将查询图像转换为HSV颜色空间
$hsv = $cv->cvtColor($query, CV_BGR2HSV);

// 计算查询图像的颜色直方图
$queryHist = $cv->calcHist(array($hsv), array(0, 1), null, array(180, 256), array(0, 180, 0, 256));

// 归一化查询图像的颜色直方图
$queryHist = $cv->normalize($queryHist, 1);

// 获取数据库中的图像列表
$result = mysqli_query($conn, "SELECT * FROM images");

while ($row = mysqli_fetch_assoc($result)) {
    $id = $row['id'];
    $hist = explode(',', $row['hist']);

    // 将数据库中的颜色直方图转换为数组
    $data[] = array_map('intval', $hist);

    // 计算相似度
    $similarity = $cv->compareHist($queryHist, $hist, CV_COMP_CORREL);

    // 存入相似度列表
    $list[] = array('id' => $id, 'similarity' => $similarity);
}

// 按相似度降序排序
usort($list, function ($a, $b) {
    return $b['similarity'] - $a['similarity'];
});

// 输出相似图像的列表
foreach ($list as $item) {
    echo $item['id'], "    ", $item['similarity'], "
";
}

// 关闭数据库连接
mysqli_close($conn);
?>

3. Summary

High-speed image retrieval algorithm is an important method in image retrieval. It can quickly find and query images in massive image data. Similar images. In PHP, you can use the OpenCV library to implement a high-speed image retrieval algorithm based on color histograms. Through the introduction of this article, I believe readers can understand the principles of high-speed image retrieval algorithms and their implementation methods in PHP.

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