Post by sumaiya12 on Oct 31, 2024 5:49:02 GMT -5
In the leading search engines, you can find images by a set of keywords. But these platforms have an even more impressive, although not so popular, function. It allows you to find the same or very similar image on other resources. Let's figure out how to perform a reverse image search, why it is necessary and what the sequence of your actions should be.
How to Use Reverse Image Search on Different Devices
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What is reverse image search?
Let's say you have a photo. You've sent it to your family and friends. And you want to check if anyone has uploaded it to social networks and open media banks. In this case, reverse image search comes to the rescue.
It allows you to check whether
content writing service an identical image exists on the Internet. With the development of artificial intelligence technologies, search engines began to display similar images on the results page - heavily edited, with similar content or with the same people in sight.
Most leading search engines have similar features, including Google, Bing, and Yandex. There are also specialized services that use their own algorithms, such as TinEye and Pixsy. They are usually more accurate, so they are used by professionals - designers, photographers, webmasters, and technical SEO specialists.
How does reverse image search work?
To answer this question, it is worth delving into the technical nuances. Digital multimedia is based on a mathematical function called the Fourier transform. It allows you to reproduce the sequence of multi-colored pixels as a code of ones and zeros. Thanks to this, it is possible to obtain images and photographs that take up a relatively small amount of memory.
Image backtrace takes this function in reverse—hence the process's common name. It converts an image into code and starts comparing it with open-source information. How effective is this process?
The answer will surprise you: 10 years ago, before the widespread use of artificial intelligence, search engines were already good at this function. This is due to the peculiarities of converting images into mathematical expressions. The code is divided into fragments, each of which has a unique signature or checksum. Therefore, the search engine does not compare general data arrays, but only point samples. Even a home computer can perform this task, not to mention the super-powerful servers of Google, Bing and TinEye.
The digital signature system makes images truly unique. The chance that the sequence of pixels and checksums of two images will be the same is 1 in 100 billion. For comparison: the probability of matching fingerprints is 1 in 64 billion. That is, images on the Internet are even more unique than you and I.
If the reverse photo search system worked well 10 years ago, then what was the use of artificial intelligence for? Self-trained neural networks have expanded its capabilities - they have given users the opportunity to search not only for identical but also for similar images. Examples of such search results:
heavily edited photos;
pictures with similar layout and color scheme;
works by one artist;
images that contain the same people;
automatically generated images with the same construction principle.
Unlike conventional search systems, neural networks look at the location of pixels, not the program code. They use various processing algorithms that simplify the image as much as possible. In some developments, raster images are converted into vectors, that is, a set of reference points and lines. In other images, they are compressed to 128–256 pixels, and in others, geometric figures replace the main objects.
a). Structure of the neural network. The feature maps are shown in... | Download Scientific Diagram
But the further principle of operation is very similar. The neural network builds an image map and describes it with very simple means - keywords or code fragments. This allows you to quickly compare very similar, but still not identical images. Since such developments are still inaccurate, associative search systems are connected to them. They evaluate the context of the image use on the page, the connection with other images, links, keywords and other SEO elements. This increases the accuracy of the search and increases the issuance.
Why do people use the feature?
Reverse photo lookup was initially considered an interesting but impractical technology. However, with the development of neural networks and changes in the principles of website optimization, more and more people began to pay attention to it. Today, there are several reasons to use this function both at the household and professional level.
Search similar images
For example, you are looking for a replacement for your desktop wallpaper, but do not want any fundamental changes. Or you need to select images for your website so as to maintain information content and not repeat competitors.