Object retrieval problem in visual search

Target retrieval issues in visual search require specific code examples

With the continuous development of technology and the growing demand for information acquisition, visual search has gradually become a popular technology. Visual search is a technology that obtains relevant information through image or video content. It can analyze and identify objects, scenes, etc. in images or videos, and realize automatic annotation, classification, and retrieval of images or videos. In the object retrieval problem in visual search, we need to input an image to search for similar images. This article will introduce the target retrieval problem in visual search and give specific code examples to help readers better understand and practice this technology.

In the target retrieval problem in visual search, the most critical thing is how to calculate the similarity of images. Common image similarity calculation methods include Euclidean distance, cosine similarity, etc. The following is an image similarity calculation function based on Euclidean distance:

import numpy as np

def euclidean_distance(img1, img2):
    # 将图像转换为灰度图并将其转换为numpy数组
    img1 = np.array(img1.convert("L"))
    img2 = np.array(img2.convert("L"))
    
    # 计算两个图像的差异
    diff = img1 - img2
    
    # 将差异平方并累加得到欧氏距离的平方
    euclidean_distance = np.sqrt(np.sum(np.square(diff)))
    
    return euclidean_distance

In addition to calculating the similarity of images, we also need to build an image database to store and manage images. The following is a code example of a simple image database class:

class ImageDatabase:
    def __init__(self):
        self.images = []
        
    def add_image(self, image):
        self.images.append(image)
        
    def search_similar_images(self, target_image, num_results=10):
        # 计算目标图像与数据库中其他图像的相似度
        similarities = []
        for image in self.images:
            similarity = euclidean_distance(target_image, image)
            similarities.append(similarity)
            
        # 按相似度从小到大排序
        sorted_indices = np.argsort(similarities)
        
        # 返回相似度最高的前num_results个图像
        similar_images = [self.images[i] for i in sorted_indices[:num_results]]
        
        return similar_images

Using the above code example, we can easily implement a simple image target retrieval system. First, we need to create an image database and add some images into it:

database = ImageDatabase()
database.add_image(image1)
database.add_image(image2)
database.add_image(image3)
...

Then, we can search for similar images by entering a target image:

target_image = load_image("target.jpg")
similar_images = database.search_similar_images(target_image)

With the above code, we can get The top 10 images that are most similar to the target image and undergo further processing and analysis.

It should be noted that the above code is just a simple example, and the actual visual search system may require more complex algorithms and technical support. However, through this simple code example, readers can initially understand and experience the target retrieval problem in visual search, and proceed with further learning and practice. Hope this article can be helpful to everyone!

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