Image Recognition: What Is It & How Does It Work?

Teal blog header for image recognition
Teal blog header for image recognition

Using Google Lens to find those stylish chairs you just saw in a cafe, unlocking the phone with your face, self-driving cars stopping at a red light, tagging your friends online—none of this would be possible without image recognition.

Since image recognition is increasingly important in daily life, we want to shed some light on the topic.

Table of Contents

What Is Image Recognition?

Image recognition is a type of artificial intelligence (AI) that refers to a software‘s ability to recognize places, objects, people, actions, animals, or text from an image or video.

A silver number four on an orange background

4 Image Recognition Techniques

Image recognition consists of four main techniques:

  1. Classification: The goal of classification is to identify the category into which a specific image fits.
  2. Tagging/labeling: This is a type of classification, but with a higher level of accuracy. For example, several objects can be tagged and labeled within one image.
  3. Object detection: Detection is used to locate a particular object within an image. After the object is detected, a bounding box is placed around it.
  4. Segmentation: With segmentation, an element of an image can be localized down to the nearest pixel.

Image Recognition vs. Computer Vision & Co.

Before we move on, let’s briefly tap into some terminology you might encounter within the context of AI image recognition: computer vision, machine learning, and deep learning.

Computer vision

Computer vision is a set of techniques that enable computers to identify important information from images, videos, or other visual inputs and take automated actions based on it. In other words, it’s a process of training computers to “see” and then “act." Image recognition is a subcategory of computer vision.

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Machine learning

Machine learning is a subset of AI that strives to complete certain tasks by predictions based on inputs and algorithms. For example, a computer system trained with an algorithm of images of cats would eventually learn to identify pictures of cats by itself.

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Deep learning

Deep learning is a subcategory of machine learning where artificial neural networks (aka. algorithms mimicking our brain) learn from large amounts of data.

Neural networks

Neural networks are a type of machine learning modeled after the human brain. Here's a cool video that explains what neural networks are and how they work in more depth.

Chapter one complete! 🥳 We’ll continue by answering…

How Does Image Recognition Work?

How image recognition works in four steps.

  • Step 1: Extraction of pixel features of an image
  • Step 2: Preparation of labeled images to train the model
  • Step 3: Training the model to recognize images
  • Step 4: Recognition of new images

Let’s break those down.

Step 1: Extraction of Pixel Features of an Image

In the first step of AI image recognition, a large number of characteristics (called features) are extracted from an image. An image consists of pixels that are each assigned a number or a set that describes its color depth.

Image of an illustrated dog with image recognition

Step 2: Preparation of Labeled Images to Train the Model

After the image is broken down into thousands of individual features, the components are labeled to train the model to recognize them.

The golden rule: The more labeled components there are, the more accurately the model can be trained.

Different illustrations of cats, dogs, and trees to show categorizing in image recognition

Step 3: Training the Model to Recognize Images

The actual training of the model takes place during this step. The images are inserted into an artificial neural network, which acts as a large filter. Extracted images are then added to the input and the labels to the output side.

The goal is to train neural networks so that an image coming from the input will match the right label at the output.

Illustration of how image recognition works step 3

Step 4: Recognition of New Images

After the training, the model can be used to recognize unknown, new images. However, this is only possible if it has been trained with enough data to correctly label new images on its own.

Illustration of showing how to train image recognition algorithms with dogs, cats, and trees

Before we wrap up, let’s have a look at how image recognition is put into practice.

Image Recognition Examples

Many industries have already discovered the benefits and possibilities of AI-powered image recognition, including:


Thanks to image recognition software, online shopping has never been as fast and simple as it is today.

For example, the mobile app of the fashion retailer ASOS encourages customers to take photos of desired fashion items on the go or upload screenshots from all kinds of media.

The app's AI algorithm then scans the image and shows customers similar products available in the ASOS shop.

Did you see someone wearing an amazing outfit as you were sipping coffee? No problem! Snap away and start shopping. 📷

Screenshots from the ASOS mobile app using online image recognition


In the financial sector, banks are increasingly using image recognition to verify the identities of their customers, such as at ATMs for cash withdrawals or bank transfers.

Some also use image recognition to ensure that only authorized personnel has access to certain areas within banks.

Automotive Industry

Driving is becoming more and more autonomous. Today's vehicles are equipped with state-of-the-art image recognition technologies enabling them to perceive and analyze the surroundings (e.g. other vehicles, pedestrians, cyclists, or traffic signs) in real-time.

With AI-powered image recognition, engineers aim to minimize human error, prevent car accidents, and counteract loss of control on the road.

The interior of a self-driving Tesla car

Image Recognition and Marketing

Now, you should have a better idea of what image recognition entails and its versatile use in everyday life. In marketing, image recognition technology enables visual listening, the practice of monitoring and analyzing images online.

Similar to social listening, visual listening lets marketers monitor visual brand mentions and other important entities like logos, objects, and notable people. With so much online conversation happening through images, it's a crucial digital marketing tool.

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  • ✓ Gain brand insights from visual content posted on news, blogs, and Reddit
  • ✓ Detect and classify brand mentions in memes with 90%+ accuracy
  • ✓ Analyze image sentiment with visual emotion detection
  • ✓ Use age group classification, gender classification, and people density recognition for consumer insights
  • ✓ Search for objects, celebrities, scenes, and much more!
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