Konstantin Sadekov
May 30th, 2022
From startups to multinational organizations, different types of sellers and service providers realize the growing need for sophisticated data processing. Digitizing the various manual processes of data collection, analysis, and everything in between, has enabled businesses to achieve higher levels of productivity and made our everyday lives easier.
One of the technologies that have played a key role in this revolution is image recognition, a key sub-task of computer vision, which is the science of enabling computers to interpret visual data such as images and videos.
From answering seemingly simple questions such as “Is that broccoli?” to carrying out complex analytical processes in large-scale industries, we have witnessed the astonishing rates at which image recognition and related computer vision tasks have expanded. The improvements witnessed in the field of artificial intelligence (AI) have made the performance of AI image recognition algorithms even better and faster.
In this article, we will take a deep dive into the following:
What is AI image recognition technology?
How image recognition works
Image recognition business use cases and examples
Business benefits of AI image recognition
Image recognition, also called image classification, is a key task in the ever-growing field of computer vision that pertains to identifying certain types, aka classes, of objects within an image or video frame, applying machine learning techniques. Here is an example of an image recognition task that identifies objects such as trees and humans in a picture of a landscape.
Image recognition can be carried out through simple image processing methods such as deterministic algorithms. However, these techniques can be quite restrictive in functionality and scope. The integration of artificial intelligence into image recognition methods, while making the process more complex, has greatly expanded their horizons.
Artificial intelligence and machine learning (ML) empower modern image recognition systems to pick up hidden patterns – even those not apparent to the human eye – in collections of images and make independent, smart decisions. AI image recognition has greatly reduced the need for machines to get input and/or feedback from human agents, enabling the automated processing of visual data streams on increasing scales.
To clarify, image recognition is a subset of computer vision, which encompasses various tasks related to processing and analyzing images and videos. It typically requires multiple tasks like object detection, image identification, and classification. It’s crucial to distinguish this from related concepts like object localization, which finds and marks object locations without classifying them, and image detection, which focuses on identifying and counting distinct entities without necessarily recognizing their significance.
For instance, a practical use case of image recognition is object detection for mask detection, where software not only identifies but also locates safety equipment in images and videos. This is different from image detection, which would only find and count the equipment without categorizing them. Understanding these distinctions is essential for accurately implementing specific workflows in computer vision.
AI image recognition technology is a core application of deep learning. Despite evolving methods to mimic human vision, the primary goal remains to classify detected objects into various categories. However, in their quest to imitate the logic that the human brain functions on, AI systems have superseded us in many ways, e.g., by being faster, more attentive, and able to easily handle big data.
One of the most widespread underlying machine learning concepts that image recognition models apply is neural networks, which are loosely based on our current scientific understanding of the human brain. Neural nets replicate the biological neural mapping that human brains utilize for processing and analyzing information.
The process of image recognition has three main steps:
The algorithm is first taught, using a training dataset, what to expect from the input data. If, for example, you want a system that would identify images containing different types of animals or other objects in a picture, this is what the training dataset might look like:
After carefully studying the training data, the image recognition system forms meaningful associations between the images and the expected outputs. The system is then evaluated on what it has learned using a test dataset, e.g., is the system good enough at identifying images containing cars.
It can take a few (or many!) tries before you obtain acceptable results, depending on the quantity and quality of data used for training. Once the system reaches an accuracy level that meets your requirements, it can be used to make predictions based on real data; this is the final stage of the process.
There are three common methods of training image recognition systems – supervised, unsupervised, and self-supervised learning. For a more detailed explanation of the first two techniques, you can check out our article on computer vision machine learning, but here’s a quick overview.
The primary difference between all three training methods lies in the labeling of the training data.
For example, if you want the image classification system to be able to identify images of cars, you can use two labels, CAR and NOT CAR.
If you explicitly label both types of images in the input data beforehand, it will fall under supervised learning.
For an unsupervised model, you can simply provide a set of images to the image recognition model without stating what the images contain.
The system will have to figure out on its own the meaningful similarities or differences between the images by studying their characteristics or features.
Self-supervised training also makes use of unlabeled data, which is why it is often considered a subset of unsupervised learning. It is a learning task where pseudo-labels, generated from the data itself, are used for learning. It allows you to use lower-quality data to learn to represent the data. This can be used as a base for many tasks, e.g., you use self-supervision to teach the machine to recreate human faces. When you are done training the algorithm, you can give it novel input to have it generate completely new faces.
Now, let’s talk about the two common types of image recognition systems, binary and multiclass.
We can use the same CAR identification example here. If you want the algorithm to clearly identify which images contain cars and which ones don’t, this will constitute a binary classification problem.
What happens if we add cycles to the mix? This is now a multiclass problem as the possible answers to a certain query are CAR, CYCLE, or NEITHER.
The widespread use of image recognition has enabled us to move far beyond the simple examples we have discussed so far. Many different industries including security, healthcare, education, fintech, manufacturing, telecom, utility, and defense, are rapidly adopting image recognition systems to make their digital images or video data processing and analysis capabilities faster, more accurate, and more efficient.
Read on to learn about some of the top applications of image recognition.
While object detection is not exactly an application of image recognition, it is important to acknowledge the crucial link between the two. Object detection builds upon image recognition by adding the element of localization. This enables the algorithm to not only recognize a particular object in an image or video but also point out its location.
A primary application of object detection is seen in the automated fault detection process in the manufacturing industry. MindTitan worked on one such project for Hepta Airborne, about which you can learn more in our computer vision case study.
Hepta provides automated asset management services to utility companies. Their product uses drones to conveniently photograph power lines. The digital images gathered by the drones is supplied to the object detection model, which analyzes the images to rapidly detect energy transmission network faults. The automation of this process has resulted in better preventative maintenance of power grids.
The medical and fitness industries also apply object detection and image recognition in various areas. The traditional methods of medical diagnosis have undergone major advancements by embracing image recognition software. Machines now assist medical professionals with analyzing medical imaging data, e.g., MRI and CT scan results, to quickly and accurately detect potentially fatal illnesses such as tumors, cancers, and blood clots.
Some other examples that we discuss further on in this article, such as license plate recognition, face detection, and OCR (optical character recognition), also make use of image recognition in conjunction with object detection.
Optical character recognition, commonly known as OCR, is a technique of converting handwritten or printed text into a digital format in order to make it machine-understandable. It is perhaps one of the most widely implemented applications of image recognition.
Text is provided to the machine in the form of images. Certain computer vision and image recognition algorithms are run on the images to analyze and decode them and pick up each individual letter from the text. Once this text is digitized, it can be easier to read, edit, store, and search through on a computer system. Important data can be easily extracted from paper-based documents once they have been digitized.
There is a multitude of industries and areas where OCR can be seen in action. For example, airport security uses it to verify ID and passport validity, while in traffic surveillance, optical character recognition allows the identification and tracking of license plates of vehicles breaking the law. The highly advanced OCR system implemented in the Google Translate app provides you with real-time translation services. You simply photograph a piece of text written in a foreign language and the app will translate it to a language of your choice immediately.
Face or facial recognition technology uses deep learning algorithms to analyze a photo of a person and output the exact identity of the person present in the image. The algorithm can be built upon to extract important details such as age, sex, and facial expressions.
The applications of facial recognition systems are getting increasingly mainstream every day. Modern-day algorithms can identify people by face so accurately that they are used for access control mechanisms such as smartphone locks and private property entrances.
Computerized photo ID verification at security checkpoints such as those at airports or building entrances has also become possible with face recognition algorithms. Another application of facial recognition in the field of law enforcement is seen when locating missing persons or wanted criminals using area-wide surveillance video feeds.
You might have seen facial recognition algorithms being used by social media platforms too. When you upload a new photo of your friends on Facebook, for example, the app automatically suggests the friends whom it thinks are in the photo.
Detecting financial, electronic, insurance, identity and other types of fraud is a matter of critical importance. With advanced AI image recognition techniques, it is possible to automate and improve the process of fraud detection.
Using AI image recognition for processing cheques (or other documents) submitted to banks is one way to detect fraud. The machine analyses scanned images of the cheque to extract important features such as account number, cheque number, cheque size, and account holder’s signature, to determine the authenticity and validity of the cheque.
Fraudsters can also take the route of identity theft, where they may use a fake identification document and pretend to be someone else. Buying prescription drugs or obtaining credit on the basis of a stolen ID, for example, can be swiftly detected and prevented with image recognition-powered ID verification checks including biometric scans.
Another application is seen in insurance fraud detection where the validity of insurance claims can be determined by conducting thorough image and video analysis. Human agents can often miss crucial details when going through visual data collected from the scene of a crime or accident. With AI image recognition, the machine can analyze multiple images to ascertain the cause of the accident, the level of loss or damage incurred, or even the authenticity of the image itself, all based on contextual clues or metadata picked up from the images.
Just as Google’s Translate app allows real-time translation by reading text from images, Google Lens enables users to perform image-based searches. The technology has evolved to offer in-the-moment searches to its users. Found a beautiful flower at the picnic and wondering what kind it is? Take a photo to search it up on the spot. MindTitan also built a custom visual search engine for one of its clients. This is just one of the many applications of visual search.
Visual search is slowly gaining traction as image classification methods strive to take us one step ahead of text- or even voice-based search. The input is always in the form of an image. The result can either be text-based, such as an explanation for the input image, or image-based, such as other similar-looking images.
Face recognition apps that accept user images as input and then find a match in an existing database are one application of visual search. Another example is the reverse search that you might have done at some point in life to figure out if you’re being catfished on Tinder! Visual search is also used often in online retail where customers can just upload pictures of what they want to buy instead of struggling to find the right keywords to accurately describe what they’re looking for.
AI image recognition has been driving the world towards improved accessibility for differently-abled individuals. Teaching machines to extract important features from images helps generate labels or full-fledged image descriptions.
Image recognition models can not only detect the text found in the image using OCR but also the different kinds of objects or humans present. They can be trained to describe images in quite a bit of detail, going over things such as the age, action, and facial expressions of the person(s) present or the overall scenery detected in the image.
You might have seen this in practice on various social media platforms where, in case of missing alternate text, a description is automatically generated and added to the image. This advancement has provided a great benefit to screen readers, which can now describe even those images which might not be explicitly labeled or accompanied with descriptions. It provides an improved, more inclusive experience to visually impaired users.
Have you ever received a warning on Facebook for not following community standards?
Facebook’s systems use AI to automatically detect and flag content that they deem not suitable for posting on the social media platform. Based on the degree of the offense, you are given a warning or your account restricted for a certain period of time. You can appeal this automatic decision; your case is forwarded to human agents who manually review the flagged content and decide whether or not the system made a mistake.
An image-based content moderation or filtering system would work on similar principles. Manual content moderation would be highly resource-intensive and time-consuming – imagine operating at the level that Facebook operates on and reviewing an unbelievably high volume of data image by image.
You can train an AI image recognition algorithm to detect certain types of images, e.g., inappropriate visual content such as adult content, violence, or spam. The system can then take appropriate action without the need for human intervention. This will make the moderation process faster, cheaper, and more efficient. Not only that, but you will also spare yourself or other human agents from having to see potentially traumatizing content.
From small-scale features to full-fledged organization-wide implementations, you can achieve varying levels of automation with computer vision. This can significantly reduce the amount of effort and intervention required from human agents.
An automated system drastically reduces the number of work hours that need to be put into certain processes such as identity confirmation or signature authentication. Your team can work marginally smarter instead of harder by delegating repetitive, monotonous tasks to machines. Consequently, you can focus your energy and valuable resources on the more creative business functions.
Many image recognition systems have proven to be much better, faster, and more accurate, performing better than their human counterparts. You can achieve speedy results with image recognition systems, getting more done in much less time, and also slash labor costs, among other overheads, in the process.
Furthermore, business owners are able to gain valuable insights from visual data in real-time, enabling them to implement timely business decisions based on the results gathered from image recognition systems. For example, certain critical insights regarding consumer behavior obtained from image recognition systems can be used to deliver highly focused, targeted content and provide personalized experiences to your customers, boosting visibility, engagement, and revenue.
Having delivered over 80 projects involving cutting-edge artificial intelligence (AI) applications, we can attest to the fact that computer-aided data processing yields noteworthy benefits concerning efficiency, productivity, and profitability.
MindTitan offers computer vision services that help to solve complicated business problems when off-the-shelf solutions are not able to help or when it requires integration with other AI models.
