Image recognition is the ability of software to identify objects, places, people, and actions in images. Using machine learning datasets, enterprises can use image recognition to identify and classify objects into several categories.

Although humans are much better than machines in visual performance due to superior high-level image understanding, contextual language, and parallel processing. But, after a certain period of time, human capabilities are not enough to handle complex business processing. This being the reason has paved the way for the automatic image recognition system.

As per Research and Markets report, the image recognition market is expected to reach USD 4.5 Bn by 2026.

Tech giants like Google, and Amazon, are offering image recognition services to get a boost on excellent customer service.

And why not? With advancements in computing capability and image processing technology, the adoption rate of image recognition is getting high among enterprises. Let’s dig into this blog and find out what image recognition is, how it works, and where you can use it.

What is Image Recognition?

When you see the object, how do you identify, classify, and categorize the objects? It requires vision and contextual understanding.

The image recognition system works in the same, Image recognition is the technology that identifies a place, people, images, objects, and several other variables present in digital images.

A digital image is an image consisting of picture elements also known as pixels with finite and discrete quantities of numeric representation for its intensity and grey level. That’s where the image recognition role comes in.

Image recognition uses a set of algorithms and techniques to label and classify the elements present inside the image. It mainly focuses on the content of the image.

Enterprise can train image recognition model to take input images and output previously classified labels that defined the image.

How does Image Recognition work?

Basically, the task of image recognition involves the formation of a neural network that can process individual pixels of an image. To make it more effective, this neural network can be fed with pre-labeled images to teach them how to recognize similar images.

Basically, this whole process works in three steps-

  • Firstly, an image recognition system requires a dataset that contains images with their respective labels. For example, an image of a “cat” must be labeled with “cat” to identify the objects.
  • Secondly, these images need to be fed into neural networks and then need to be trained to see specific patterns in the images to identify the content of an image. This is called feature extraction. 
  • Thirdly, the image dataset can be trained in the image recognition model to predict certain objects and label the input image into a certain class.

Image Recognition Use Cases across Industries

Retail Industry

Getting shoppers to your e-commerce is one thing, but getting them to purchase from your platform is a big hill to climb up.

But image recognition tool helps the customers to use visual research to look for similar products using a reference image downloaded from the internet.

Also, the image recognition feature enables retailers to offer personalized customer experience, easy product recommendations, and enhanced product discovery. In addition, image recognition helps retailers in fashion trend analysis, counterfeit product detection, and user-generated content analysis to make their business strategy as per market standards.

Automotive Industry

As the world is getting faster with technologies, self-assistance is also rising in the automotive industry.  From Ford to Tesla, these multi giants companies are leveraging image recognition in offering autonomous vehicles.

With image recognition features, enterprises can offer accident prevention capability by monitoring drivers’ reactions and biometrics to ensure there is no risk of falling asleep while driving and blocking the car in case of an accident.

In addition, the image recognition feature also offers efficiency for autonomous vehicles by scanning the danger and transmitting the information to other vehicles to avoid accidents and no traffic jams.

Healthcare

It is almost impossible to think about the field of medicine and healthcare management without harnessing technologies like X-rays, MRIs and etc.

As technologies evolve, the adoption of image recognition features is highly scalable in the healthcare industry. Training image recognition models can help in scanning images from X-rays, MRIs and other visual outputs to detect, locate and flag up medical abnormalities, Also, the medical institution can also train image recognition models to identify small malignant tumors that are not visible to human eyes.

Such benefits are crucial for healthcare professionals in dealing with a large-scale unusual event that leads to an emergency. Ultimately, image recognition provides healthcare professionals a means of retrieving information on similar conditions and assisting in the right diagnoses and better patient care.

Security

Image or facial recognition is crucial in the security industry due to enhance capabilities of solutions like video security, access control, and identity management. Using image recognition datasets enterprises can help customer secure their facilities and employees against the threat of violence, theft, or other harm.

Also, leveraging image recognition helps in criminal identification, surveillance, police authorities, tracking employee attendance, airport security check, online transactions, defense services, etc.

Get powerful and flexible with image recognition

Technologies for sure have advanced like never before. All it required is to use it in an appropriate place. Right from the invention to accessibility, image recognition and AI capabilities are here to stay.And who doesn’t want an extra pair of eyes that can skim through digital images and classify the objects with minimal manual intervention? The right time is now to act on and leverage these technologies for better customer service.

Author

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives. Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/

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