TLDR: Image recognition has a long history, going all the way back to 1956, however, this is most likely the age when it will be seen as coming into its own. It is an incredible piece of visual artificial intelligence tech that works by analyzing an image in comparison to a learned data set so that it can “see” and interpret what is present in the visual media. Image recognition plays a strong role throughout a huge range of industries and is often an indisposable piece of technology.
When technology historians look back at the current age, it will likely be considered as the period when image recognition came into its own.
Most of us use image recognition daily without even realizing it. Everything from barcode scanners to facial recognition on smartphone cameras relies on image recognition. But it goes far deeper than this, AI is transforming the technology into something so powerful we are only just beginning to comprehend how far it can take us.
This article takes a deep dive into image recognition. We take a look at its history, the technologies behind it, how it is being used and what the future holds.
There is no single date that signals the birth of image recognition as a technology. But, one potential start date that we could choose is a seminar that took place at Dartmouth College in 1956. This seminar brought scientists from separate fields together to discuss the potential of developing machines with the ability to think. In essence, this seminar could be considered the birth of Artificial Intelligence.
However, despite early optimism, AI proved an elusive technology that serially failed to live up to expectations. Throughout the sixties and into the seventies, scientists and researchers struggled to make any meaningful progress, and with funding and optimism drying up, AI was a concept that seemed restricted to the realm of science fiction.
This all changed as computer hardware rapidly evolved from the late eighties onwards. With costs dropping and processing power soaring, rudimentary algorithms and neural networks were developed that finally allowed AI to live up to early expectations.
In terms of Image recognition, the watershed moment came in 2012. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) was when the moment occurred. The ILSVRC is an annual competition where research teams use a given data set to test image classification algorithms.
Up until 2012, the winners of the competition usually won with an error rate that hovered around 25% – 30%. This all changed in 2012 when a team of researchers from the University of Toronto, using a deep neural network called AlexNet, achieved an error rate of 16.4%.
Error rates continued to fall in the following years, and deep neural networks established themselves as the foundation for AI and image recognition tasks.
At its most basic level, Image Recognition could be described as mimicry of human vision. Our vision capabilities have evolved to quickly assimilate, contextualize, and react to what we are seeing.
This is what image processing does too – Image recognition can categorize and identify the data in images and take appropriate action based on the context of the search.
In simple terms, the process of image recognition can be broken down into 3 distinct steps.
The dataset provides all the information necessary for the AI behind image recognition to understand the data it “sees” in images.
In layman’s terms, a convolutional neural network is a network that uses a series of filters to identify the data held within an image.
The picture to be scanned is “sliced” into pixel blocks that are then compared against the appropriate filters where similarities are detected. The result is flagged as a high value in the output matrix.
This is a hugely simplified take on how a convolutional neural network functions, but it does give a flavor of how the process works.
The next obvious question is just what uses can image recognition be put to. Google image searches and the ability to filter phone images based on a simple text search are everyday examples of how this technology benefits us in everyday life.
But it is business that is unlocking the true potential of image processing. There are huge incentives for businesses to tap this resource. According to Statista, Facebook and Instagram users alone add over 300,000 images to these platforms each minute. In today’s world, where data can be a business’s most valuable asset, the information in images cannot be ignored.
For many businesses, the use of image recognition is now a critical part of their operations, amongst common uses are:
Image recognition is increasingly used by brands in the fight against counterfeiting. It can identify the illicit use of logos, trademarks, or other unique design elements.
Because Visual AI can process batches of millions of images at a time, it is a powerful new tool in the fight against copyright infringement and counterfeiting. Neural networks can quickly be trained to learn any design element.
This ability to quickly learn is a relatively new development. Previously this used to be a cumbersome process that required numerous sample images, but now some visual AI systems only require a single example.
Phishing is a growing problem that costs businesses billions of pounds per year. Traditional systems rely heavily on blacklists. However, there is a fundamental problem with blacklists that leaves the whole procedure vulnerable to opportunistic “bad actors”.
The problem is simply that a blacklist needs to be current, a few hours out of date, and the door is open for new threats to slip through. Current cybersecurity techniques also rely on identifying programmed threats embedded in emails. However, in the ongoing arms race between bad actors and security professionals, the latter are largely playing a game of catch-up.
Image Recognition, when used as part of a security stack, relies on none of these methods. Rather it scans emails and web pages, looking for visual clues from high-risk elements.
The internet is awash with illicitly streamed content. The scale of the problem has, until now, made the job of policing this a thankless and ultimately pointless task. The sheer scale of the problem was too large for existing detection technologies to cope with.
The problem has always been keeping up with the pirates, take one stream down, and in the blink of an eye, it is replaced by another or several others. Image detection can detect illegally streamed content in real-time and, for the first time, can react to pirated content faster than the pirates can react.
Social media has rapidly grown to become an integral part of any business’s brand. However, it does have unique challenges. It also can be damaging if not policed properly. Many of these problems can be directly addressed using image recognition.
The control over what content appears on social media channels is somewhere that businesses are exposed to potentially brand-damaging and, in some cases, illegal content. Image detection technology can act as a “moderator” to ensure that no improper or unsuitable content appears on your channels.
It can also be used to assess an organization’s “social media” saturation. The ability to quickly scan and identify the content of millions of images enables businesses to monitor their social media presence.
These are only a few of the real-world uses of image recognition. There are already a multitude of other uses. But the really exciting part is just where the technology goes in the future.
Here are just a few examples of where image recognition is likely to change the way we work and play.
The importance of image recognition technology cannot be overstated. Despite being a relatively new technology, it is already in widespread use for both business and personal purposes.
Companies like Google, Amazon, Microsoft, and we here at VISUA are pushing the boundaries of this technology, and the exponential rise of computer vision is only just beginning.
There are plenty more articles that take an in-depth look at the subject on our website, including this excellent article that goes into the AI powering the Visual-AI platform in greater detail.
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