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Image recognition (part of Artificial Intelligence (AI)) is another popular trend from gathering momentum nowadays — by 2021, its market is expected to reach almost USD 39 billion! So now it is time for you to join the trend and learn what image recognition is and how it works. No worries, we will explain everything in detail. And, obviously, we will also talk about artificial intelligence and machine learning. Their advancements are the basis of the evolution of image recognition technology. Keep reading to discover more.

Image Recognition: What It Is and How It Works

As always, let’s start with the basics. First of all, you should remember is that image recognition and image processing are not synonyms. Image processing means converting an image into a digital form and performing certain operations on it. As a result, it is possible to extract some information from such an image.

Image processing stages

  • Color image processing — the colors are processed
  • Image enhancement — the quality of the image is improved and the hidden details are extracted
  • Image restoration — the image is cleaned from blurs and other unpleasant things
  • Representation and description — the processes data is visualized
  • Image acquisition — the image is captured and converted
  • Image compression and decompression — the size and resolution of the image are changed, if necessary
  • Morphological processing — the structure of the image’s objects is described
  • Image recognition — specific features of the image’s objects are identified

Image recognition

Now you see that image recognition is one of the image processing stages. Those specific features which we mentioned include people, places, buildings, actions, logos and other possible variables in the images. Therefore, image recognition is a process of identifying and detecting an object in a digital image, and one of the uses of computer vision. Sometimes it is also called image classification, and it is applied in more and more industries. One of them is e-commerce.

For instance, Boohoo, an online retailer, developed an app with the visual search feature. A user simply snaps an item they like, upload the picture, and the technology does the rest. Thanks to image recognition, a user sees if Boohoo offers something similar and doesn’t waste loads of time on searching for a specific item. Other applications of image recognition (already existing and potential) include creating city guides, powering self-driving cars, making augmented reality apps possible, teaching manufacturing machines to see defects, and so on. There is even an app which helps users to understand if an object of the image is a hotdog or not.

How does it works

Now, a few more words about how image recognition works. The first step here is gathering and organizing the data. Unlike humans, computers perceive a picture as a vector or raster image. So after the constructs depicting objects and features of the image are created, the computer analyzes them. Then, the data is organized — the important information is extracted, and the unnecessary one is left out. The second step of the image recognition process is building a predictive model. The classification algorithm has to be trained carefully, otherwise, it won’t be able to deliver it function. When everything is done and tested, you can enjoy the image recognition feature.

How AI Helps Image Recognition

We described how image recognition works, but you may still have a lot of questions regarding how to complete those stages. Here is an answer — do image recognition using AI. Artificial intelligence makes all the features of image recognition possible. To give you a better understanding, here are some of them:

Facial recognition

With the help of AI, a facial recognition system maps facial features from an image, and then compares this information with a database to find a match. Facial recognition is used by mobile phone makers (as a way to unlock a smartphone), social networks (recognizing people on the picture you upload and tagging them), and so on. However, such systems raise a lot of privacy concerns, as sometimes the data can be collected without a user’s permission. Apart from this, even the most advanced systems can’t guarantee a 100% accuracy. What if a facial recognition system confuses a random user with a criminal? That’s not the thing someone wants to happen, but this is still possible. However, technology is constantly evolving, so one day this problem may disappear.

Object recognition

Object recognition systems pick out and identify objects from the uploaded images (or videos). Visual search is probably the most popular application of this technology, and we have already provided several examples. The hotdog application, do you remember?

Text detection

Everything is obvious here — text detection is about detecting text and extracting it from an image.

Pattern recognition

Pattern recognition means finding and extracting specific patterns in a given image. Those can be textures, facial expressions, etc.

Image analysis

Do you need a summary of a specific image? Then use AI for picture and image analysis. As a result, all the objects of the image (shapes, colors, and so on) will be analyzed, and you will get insightful information about the picture.

Methods, Techniques and Tools for Image Processing with AI

Let’s start with the simplest things — methods of image processing. Currently, there are only two of them: analog and digital. The analog method is used for processing hard copies of images (like printouts). The mission of the digital one is to manipulate digital images using computer algorithms.

Regarding the techniques, they exist in spades, and we have already mentioned some of them. For instance, image restoration is considered both as a stage and technique of image processing. Here are some of the other techniques:

  • Pixelation — turning printed pictures into the digitized ones
  • Linear filtering — processing input signals and producing the output ones which are subject to the constraint of linearity
  • Edge detection — finding meaningful edges of the image’s objects
  • Anisotropic diffusion — reducing the image noise without removing crucial parts of the picture
  • Principal components analysis — extracting the features of the image

Tools

Fortunately, you don’t have to develop everything from scratch — you can use already existing platforms and frameworks. Cloud Vision API from Google is one of the most popular of them. Features of this platform include image labeling, text detection, Google search, detection of explicit content, and some others. If you choose this option, you will be charged per image. However, the first 1000 images used each month are free.

The next variant is Amazon Rekognition (yes, we made no mistake, that’s Rekognition). It allows adding visual analysis feature to your app, integrating face-based user verification, identifying diverse objects, detecting unsafe content, etc. Obviously, you will be charged for using the platform, but you can still try it for free.

And the last option to mention in our article — Azure Custom Vision Service. Using it, you can develop a new custom computer vision model and train it. Upload your own labeled images, tag them and improve your classifier — everything is very simple. Just like with Google and Amazon, here you pay only for what you use. A free trial is available as well.

Now you have a detailed guide on how to use AI in image processing tasks, so you can start working on your project. But in case you still have any questions (for instance, about cognitive science and artificial intelligence), we are here to help you. As always.

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