What is Image Recognition in Machine Learning

What is Image Recognition in Machine Learning

Image or Object Recognition is a computer technology in which the image interprets and identifies the objects. People sometimes mistake face recognition for the identification of images.

However, the difference is pretty clear. If you need to classify the items in the picture, use Classification. But if, for example, you only need to identify them. And also, figure out the number of objects in the frame, then you can use Image Recognition.

What is Image Recognition?

Object recognition is AI’s ability to identify, recognize, and acknowledge an object. The last stage is similar to image analysis at a human level. The best example of solutions for image recognition is face recognition. Say, you have to let it scan your face to unblock your smartphone. The system must first, therefore, identify the face. Second, categorize it as a human face and only then determine whether it belongs to the smartphone owner.

How does Image Recognition work?

Facebook can now undertake face recognition with an accuracy of 98 percent that is comparable to human ability. Facebook will recognize only a few branded pictures of your friend’s face. Moreover, the efficacy of this technology depends on grading pictures.

Classification is a matching pattern to results. Photos are data in a 2-dimensional matrix format. Image recognition actually classifies data into one out of many categories. Optical character recognition ( OCR) is a simple, and essential example. OCR converts text-typed or handwritten images into text encoded on a machine.

How to use Image Recognition?

Correspondingly, from a business perspective, major image recognition applications are:

  • Face detection.
  • Protection and monitoring.
  • Visual GPS location.
  • Object detection.
  • Hand gestures.
  • Code recognition.
  • Systems engineering.
  • Healthcare.
  • Vehicle assistance imaging.

Furthermore, let’s look at how image recognition in some business sectors is creating a revolution-


Image recognition and computer vision technologies will revolutionize the gaming world. Indeed, the transition has already started. As a result, the video game Microsoft Kinect holds Guinness World Record as the quickest-selling electronics product.

Identically, the game is based on the vision of a machine and it monitors the human body in real-time. Hardcore gamers are more inclined away from the console to the ones with real-world action. In addition, image recognition holds the key to creating these new user and interface experiences.

Again, pairing image technology blended with geo-targeting and in-app buying, search-based commerce, or ads begins to transition the real world. Opening the doors to enormous AdWords-sized, off-device market opportunities.

Automobile Industry

Likewise, an essential part of autonomous vehicles pioneered by Google and Uber is image recognition and processing. Future cars predict to detect barriers and warn you about closeness to footpaths and guard rails. The technology can even read traffic signs and stoplights.

Additionally, thousands of images train computer vision systems powered by deep learning. Images of traffic signs, people, bridges, etc. are fed into the neural networks under various environmental conditions. In the first place, as more teaching data is fed into the system, the systems get smarter.

Image Recognition in Machine Learning

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