Image Recognition: Everything You Need to Know (Guide)

Person Detection with Computer Vision

In this article we will cover image recognition, an application of Artificial Intelligence (AI) and computer vision. Specifically, you will learn:

  1. What is image recognition?
  2. How does it work?
  3. Difference to Computer Vision
  4. Difference to Object Localization
  5. Real-world use cases

What is Image Recognition?

When we process a scene in front of our eyes, we automatically identify objects as different from one another and associate them with definitions. Image recognition is a term used to describe the task of identifying images and categorizing them in one of several predefined distinct classes. The technology is a computer vision technique that allows machines to interpret and categorize what they “see” in images or videos.


Image Recognition and Object Detection
Example of image recognition

How does Image Recognition work?

Image recognition works by analyzing each pixel of an image to extract information, just like a human eye does. It then processes those pixels as a whole, and identifies patterns that relate those pixels to known classes of potential recognitions. There are a few steps that are at the backbone of every image recognition system.

Dataset Selection

The visual AI models require training data because they are made with neural networks. Neural networks need training images from an acquired dataset to create perceptions of how certain classes look.

For example, an image recognition model that detects different poses (pose estimation model) would need multiple instances of different human poses to have an idea of what makes poses unique from each other.

Neural Networks

This is the deep or machine learning aspect of creating an image recognition model. The images from the created dataset are fed into a neural network algorithm.

These algorithms make it possible for convolutional neural networks to identify differences between images. There’s a handful of existing and well-tested frameworks that are used for these purposes today.


The finished model is tested with images that it was not trained on to determine the usability and accuracy of the model. In general, somewhere between 80-90% of the original dataset is reserved for training, while the other 10-20% is reserved for testing.

The testing accuracy can come from percent confidence of accuracy per test image or the overall accuracy of correct and incorrect identifications.

Difference of Image Recognition vs Computer Vision

Image Recognition is often used interchangeably with computer vision because of the similarities between the two tasks. However, image recognition is an application of computer vision rather than a separate topic.

Software and apps based on image analytics can define what general idea or specific objects are depicted in a picture and distinguish one object from another, depending on the predefined classes. If an image recognition product is trained to detect specific objects, it can distinguish those objects from each other in a general photo or video.

The technology can also be used to associate multiple objects with one theme in order to detect the overarching idea that is happening in a photo or video. For example, image recognition programs can also be trained to associate a cake, party hat, and multiple people with a birthday party. Therefore, when all three of those objects are detected, the program will determine the video/image is portraying a birthday party.

Difference to Object Localization

Object localization is another subset of computer vision often confused with image recognition. Object localization refers to identifying the location of one or more objects in an image and drawing a bounding box around their perimeter.

Meanwhile, image recognition assigns a class label to an entire image. Object localization instead simply draws a bounding box around one or more objects in an image rather than recognizing an entire image or frame as a class.

What is Image Recognition Used for?

In all industries, the technology is becoming increasingly imperative, ranging from healthcare over agriculture to retail:

  • Medical Image Analysis: The technology is used in the medical industry extensively and is becoming a highly profitable subset of artificial intelligence. For example, there are multiple works regarding the identification of melanoma, a deadly skin cancer. Image recognition has also been implemented to detect abnormalities in breast cancer scans.
  • Animal Monitoring: Agricultural visual AI systems use novel techniques that have been trained to detect the type of animal and its actions. There is much use for animal monitoring in farming, where livestock can be monitored remotely for disease detection, changes in behavior, or giving birth.
  • Object and Pattern Detection: The technology is useful for identifying people, patterns, logos, objects, places, colors, and shapes. The customizability of image recognition allows it to be used in conjunction with multiple programs. For example, after an image recognition program is specialized to detect people, it can be used for people counting in retail settings.

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