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What Is Image Recognition?
How Does Image Recognition Work?
Top 6 Real-life Image Recognition Applications
Take Advantage of Image Recognition Applications
Users today share large amounts of data in the form of images via apps, social networks, and websites. Especially since the introduction of smartphones and high-resolution cameras, this volume has increased dramatically. It’s reported that over 50 billion images have been posted to Instagram since it first launched.
This massive image data paves the way for image recognition technology to flourish, allowing many industries to provide higher-quality and more innovative offerings, particularly in security, surveillance, and medical areas.
Indeed, the global image recognition market is expected to grow at a compound annual growth rate (CAGR) of 13.4% between 2023 and 2030, reaching $128.28 billion.
That’s why in this article, we’ll be discussing the fascinating image recognition technology, from its definition to its working and applications in our daily lives.
Image recognition refers to the ability of computers to identify and classify particular objects, places, people, text, and actions in digital images and videos. As a subset of computer vision, image recognition software analyzes, processes, and compares the visual content of an image or video to previously learned data, enabling the program to automatically perceive and comprehend objects the same way we humans could.
Image recognition with artificial intelligence has long been a research topic in computer vision. While several approaches for imitating human vision have evolved, the common goal of AI image recognition technology is the classification of observed objects into multiple categories. Thus, it is also known as object recognition. In addition, the terms image recognition, photo recognition, and picture recognition are interchangeable.
Also, it’s important to mention objection detection briefly because, while not being image recognition’s direct application, they are closely related. To be more specific, object detection incorporates localization into picture recognition. This enables the algorithm to pinpoint the location of an object in an image or video and identify it. There are various image recognition use cases alongside object detection. They include license plate recognition, face detection, and medical diagnosis, which we’ll discuss in the next section.
From the facial recognition function that unlocks smartphones to mobile check deposits on banking apps, image recognition is everywhere. We can also see its applications in medical imaging analysis to detect tumors, broken bones, and other anomalies and even in factories to discover defective items on the assembly line.
Image processing happens in many ways, including deep learning and machine learning models. The approach, however, depends on specific use cases. Deep learning algorithms, for instance, are frequently employed for more complex issues than machine learning models. Labor safety in factory automation and cancer detection in healthcare, to name a few.
Basically, image recognition systems require deep neural networks that analyze each image pixel. These networks are then provided with as many labeled images as possible during their training process.
Below are three major steps in image recognition:
First, you need an extensive data set of images and videos to analyze and annotate with significant traits or qualities. For example, assign a dog image as “dog.” If there are numerous dogs in one image, label these photos with tags or bounding boxes, depending on the task required.
But how large should a data set be? A good baseline is 1,000 images per class. Please provide as much data as possible so systems can learn from and provide more accurate outputs.
Following that, a neural network will receive input data and start getting trained on these images. Similar to the human brain, we need to teach the machine to recognize an object by exposing it to numerous examples. If all input training data has been labeled, supervised learning techniques will be employed to differentiate different object categories (e.g., a cat vs. a dog). If not, the system will leverage unsupervised learning methods to assess the various features of images and determine notable similarities and differences between them.
In this case, convolutional neural networks (CNNs) automatically identify essential features in images without human supervision. To perform these image recognition tasks, CNNs are equipped with several layers. The first one is the convolutional layer, which uses filters (or kernels) to scan the pixels in a batch of input images. By mathematically comparing the colors and shapes of the pixels, the layer derives significant features or patterns from the images, such as corners and edges.
Subsequently, CNN makes use of its first layer learnings to examine slightly bigger parts of the image, identifying more intricate details. With each layer, it continuously looks at more extensive and crucial parts of the image until it concludes what the image shows based on all the features it’s discovered.
After training, image recognition systems receive new images and videos to compare to the initial training dataset to generate predictions. This step results in the capability to conduct image classification and then decide whether or not a given element is there.
Once done, the system transforms those into actionable inferences. For example, when an autonomous vehicle recognizes a red light, it stops; when a security camera detects a weapon coming close, it sounds an alert.
Organizations have been increasingly embracing AI image recognition technology to improve the speed, accuracy, and efficiency of their image data processing and analysis capabilities.
Below are the top 6 image recognition applications in real life that you might’ve experienced firsthand.
Face or facial recognition technology analyzes a person’s image and returns the exact identity of that individual by implementing deep learning image recognition models. The algorithm can be modified to acquire essential details like age, gender, and facial expressions.
Facial recognition applications are becoming more and more popular today. Modern face detection algorithms are so accurate to give access control to devices such as smartphone locks and private property entries.
Face recognition has also enabled computerized photo ID verification at security systems/ checkpoints like airports and building entrances. Another practical use of facial recognition in law enforcement is area-wide surveillance video feeds to locate people missing or wanted criminals.
Chances are you’ve seen facial recognition adopted by social media platforms as well. When you upload a new photo of your friends to Instagram, for example, the app automatically suggests the friends it believes are in the picture.
A common way to identify fraud is to use AI image recognition technology for analyzing cheques (or other documents) submitted to banks. To assess the legitimacy and authenticity of a cheque, the machine analyzes scanned images of the cheque to gather crucial data such as the account number, cheque number, cheque size, and account holder’s signature.
Fraudsters may also determine theft by using a fake identification document to claim to be someone else. However, actions like gaining credit with a stolen ID can be quickly specified and stopped through image recognition-powered ID verification checks.
A different use is in insurance fraud detection. Bankers will be able to access the authenticity of insurance claims through comprehensive image analysis. When sifting through visual data obtained from the crime or accident scene, human agents frequently overlook important details. With AI image recognition, computers can study multiple images to determine the cause of the accident, the extent of loss or damage sustained, or even the validity of the image itself, all based on contextual clues or metadata extracted from the images.
Visual search is gradually gaining momentum as techniques for classifying images seek to go beyond text- and even voice-based search. With visual search, a picture is typically the format of the input, and the output comes in two types: text-based (e.g., an explanation for the input image) and image-based (e.g., other similar-looking or related images).
Similar to how the Google Translate app can translate text from images in real time, Google Lens enables users to conduct image-based searches. However, apart from image recognition, in order for Google to “read” text in photos and translate it into various languages, it also leverages optical character recognition.
Whereas Vecteezy, an online marketplace for photographs and graphics, applies picture recognition to make it easier for users to quickly find the image they’re looking for, regardless of whether it’s been labeled with a specific word/phrase or not.
Online retailers can also make extensive use of visual search, allowing customers to upload images of the products they wish to purchase without having to waste time finding the right keyword to describe them.
What’s more exciting is the application of face recognition apps that take user-provided images as input and use this database to discover face matches.
Have you ever received warnings from Facebook for violating the platform’s community standards?
Utilizing artificial intelligence (AI), Facebook’s systems can identify and flag content that is inappropriate for sharing on its platform. You may receive a warning or have your account blocked for a certain period, depending on how serious the offense is. You may appeal this automated decision; then your case is sent to human representatives who manually review the flagged content and determine whether the algorithm’s decision was wrong.
The same goes for image-based content moderation or filtering system operation. Only imagine what it’s like to work at the level Facebook does and go through an insanely large amount of data, image by image. Manual content filtering like that would, for sure, be immensely resource-intensive and time-consuming. That’s when AI image recognition steps in!
We can train artificial intelligence image recognition algorithms to understand specific image categories, such as adult content, violence, or spam. The system can then take proper measures accordingly and require no human intervention. This will result in a quicker, more affordable, and effective moderation process. In addition, you will avoid exposing yourself or other human agents to potentially distressing content.
There are many ways in which image recognition helps the retail sector, and task management is one of them.
Image recognition makes the product identification process more rapid and precise, facilitating the retrieval of relevant information like availability and price.
For instance, an image recognition software would be able to pinpoint each bottle or case of Pepsi that it recognizes if PepsiCo submitted photos of their shelves stocked with those products. This then lets the system use deep learning image recognition models to learn more about those shelves before coming to an accurate conclusion about which bottle/case is which.
Additionally, image recognition is helpful for customer behavior analysis, inventory management, and shelf monitoring. By taking pictures of store shelves and constantly monitoring their contents down to the individual product, businesses are able to improve their ordering procedures and record-keeping, as well as their understanding of what products to sell to whom and when.
Take GoSpotCheck product from FORM as a good example. It takes advantage of picture recognition software for companies to gain greater insight into their products at every stage of the supply chain, from how they’re stored during shipping to where they are on the shelves.
Image recognition serves a vital role in medical image analysis. It assists healthcare professionals and physicians in detecting and monitoring certain diseases and ailments with ease.
The technique may aid in noticing anomalies in medical scans like MRIs and X-rays, even in their earliest stages. It also enables health professionals to identify and track patterns of tumors or other abnormalities in medical images, resulting in more accurate diagnosis and treatment plans.
Medical diagnosis widely utilizes image recognition in three fields: radiology, ophthalmology, and pathology.
As a sub-category of computer vision, image recognition identifies objects of interest in a digital image and determines which class or category they fall under.
Numerous image recognition examples, like facial recognition, medical diagnosis, fraud detection, and visual search, now rely heavily on this technology. Image recognition adoption will undoubtedly increase in the future given the growing prevalence of digital cameras and mobile computing platforms, particularly for emerging applications like augmented reality, driverless cars, smart glasses, and consumer behavior prediction.
So, whenever you’re ready to enter this highly potential field, we’re here to assist you. At Neurond AI, we strive to enhance the value of your company by delivering only cutting-edge, impactful, and revolutionary AI solutions – image recognition service included. We adopt a trusted advising approach with our partners to help you go above and beyond.
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