Image recognition allows computers to “see” like humans using advanced machine learning and artificial intelligence. It’s not as complicated as it sounds.
When artificial intelligence, or AI, was first introduced as a solution to many broadsweep problems across vital industries like healthcare, manufacturing, and telecommunications, skepticism of its’ effectiveness was at an all-time high.
But as advanced forms of AI continue to emerge, like machine learning (ML) and deep learning (DL) for instance, more companies are turning to AI to solve their problems, regardless of their level of knowledge. The reality is AI startups are cropping up everywhere to solve problems for every business out there, lessening the information load necessary to succeed.
One of the easiest entry points for any business interested in improving their operations, reducing their waste, or compiling their data into actionable insights is image recognition. Today, we’ll cover exactly why.
What is image recognition?
Image recognition, or IR, is the series of steps it takes to identify, analyze, and interpret images from their assortment of pixels. Image recognition is a subsection of computer vision, or CV, which itself is a subsection of machine learning. We dig into the difference between IR and CV some more below.
Image recognition vs computer vision
To put it simply, computer vision is how we recreate human vision within a computer, while image recognition is just the process of how a computer processes an image. The other piece necessary to make it “real” computer vision is the computer’s ability to make inferences on what it “sees” using deep learning.
In other words, the goal of computer vision is to take action. Whatever the computer sees and interprets, it must then take another step to differentiate itself fully from image recognition. So you can consider image recognition as the act of seeing, and computer vision as the understanding of what’s seen.
How does image recognition work?
Image recognition’s simplest function is to “look” at an image, break it apart into its’ pixels or pixel groups, and interpret what it sees via an algorithm (or series of algorithms), called an artificial neural network.
We’ve already written extensively on artificial neural networks, but the easiest way to think about them is in correlation to a human’s biological neural network. These networks enable our brains to experience and learn from the world around us in real-time.
Image recognition step-by-step
To dig into the specifics, image recognition relies on convolutional neural networks (CNNs) to function. CNNs are specific to image recognition and computer vision, just our visual cortex is specific only to visual sensory inputs.
If you want to learn more about convolutional neural networks before continuing on, we wrote about them in-depth here.
Step one: Understanding the pixels
The first step to any image recognition is to identify the pixels either individually or in groups, depending on the size of the image. (Remember size refers not only to the literal image size, but the amount of data stored in it, too. They usually correlate.)
This is where the CNN comes into play. The CNN helps divide the image into however many layers necessary to fully “see” the image. These layers can be predetermined in a variety of ways, but they’re typically separated by the planes of colors, like RGB or CMYK.
Once the layers have been pulled apart and the CNN does the rest of it’s magic, the output is a classified, or identified and labeled, image. But ML algorithms aren’t
perfect and don’t have the same “obvious” understanding of the world that we have, so, in order to ensure accuracy, the model must be trained.
Step two: Train the model
Any AI has to be trained, just like any new employee. It doesn’t matter if it’s cognitive AI, deep learning, or a simple image recognition algorithm; models must be trained by humans before they can ever begin to think for themselves.
Basically, you can expect your image recognition AI to be pretty bad at first. But that’s where AI companies come into play to reduce your time spent training the algorithm. Instead, they’ll train it for you, so it’s much more prepared to complete the tasks necessary once onboarded.
Step three: Test the model
Once you’ve got enough training materials to run through and the model has been thoroughly introduced to different types of images, you can now let it run wild.
Letting your model run free isn’t the end of your work, though. Just like new employees, it will make mistakes and need to be corrected. This continuous correction is how machine learning models get more accurate over time. Eventually, the model will be so adaptive, you’ll forget you were the one training it at first!
Image recognition has already been applied in many security-intense industries such as banking, government, and even prisons.
Making a case for image recognition
Image recognition is one of the key aspects of industry 4.0 and manufacturing. Every manufacturing factory already has cameras in its facility, but the companies running said factories rarely do anything with the image data they are collecting.
But now through image recognition, and ML at large, that image data is worth gold. Machine learning models thrive with extensive data; imagine just how much image data a single factory produces in a day. That data can then be pooled into an ML model to help detect product issues or analyze quality way more accurately and faster than any human being.
How to get started with image recognition
AI can be applied to any business in any industry in any country. Image recognition is exactly the same. If you’re ready to begin exploring your options for image recognition and artificial intelligence in general, we recommend reaching out to AI experts to get the right recommendation.