![]() Thanks to the fingerprinting/indexing techniques described above, reverse image search was pretty good even before it was practical to apply AI to it. The search engine will then try to find the entries with the closest fingerprints, referred to as “image distance.” Deciding which factors to compare and how to weight them is also up to each search engine, but they’re mostly aiming to find a total image distance as close to zero as possible. When you upload a picture, it’ll go through the reverse image search engine’s fingerprinting algorithm. The second major part of the algorithm is figuring out which images are similar. Let’s say we’re looking for the following image and we need a fingerprint of it. But how do you fingerprint a picture? The steps vary depending on the algorithm, but most of them follow the same basic formula.įirst, you have to measure the image’s features, which may include color, textures, gradients, shapes, relationships between different pieces of the picture, and even things like Fourier Transforms (a method of breaking images down into sine and cosine). ![]() Pictures may actually be more unique than human fingerprints, since the odds of two pictures containing the exact same arrangement of pixels are unimaginably infinitesimal, while the chance of a fingerprint collision is around 64 billion – comparatively good odds. Since you’re not providing any words in your query, though, how do they know what to look for? And, most importantly, how do they find it? How each search engine’s reverse image search works varies, and they keep their exact algorithms under wraps, but the basic ideas are out there and are not so hard to grasp. ![]() For that, there are reverse image search engines provided by the likes of Google, TinEye, Bing, Yandex, Pixsy, and many more.
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