Image recognition is an information technology created for obtaining and understanding photos of the real world, converting them into digital information for further processing and analysis. Machine learning, knowledge base expansion, data mining, and pattern recognition are all involved in this field.
Advances in graphic image recognition have led to computers and smartphones being able to emulate human vision. Improved cameras in modern devices take high resolution pictures (above 30 MP). Then new programs extract the necessary data from these photos for a server to carry out image processing and recognition.
You may not be aware of this, but the human brain is a brilliant recognition machine because it can process a lot of information from just one picture. Just look at the picture above. If someone asks you what’s in it, what would you say? Probably that there are six people, a cat, three mobile devices, a monitor, and several icons.
A personal computer is not yet able to simultaneously produce such a volume of information from a picture or photograph and achieve this level of accuracy. However, image recognition technology has brought us closer to this kind of result.
So how do devices understand what is shown in a picture or photo? They use specialised algorithms embedded in convolutional neural networks — a specific architecture of artificial neural networks designed for efficient, automatic image recognition. The principle of operation of image recognition algorithms is to alternate convolutional and pooling layers. In the convolution process, each piece of the image is multiplied by the convolution matrix in fragments, and the result is consolidated and written into a similar position in the output image.
These operations do not actually occur on mobile devices themselves. Any smartphone, even with the most powerful hardware and software, merely sends the photo to a server, where it is processed and checked against a database. So the image recognition neural network is deployed on servers, not user devices. It turns out that in computer vision, the camera of a smartphone or laptop is just eyes. And the server, which is far from the eyes (in another city or country), acts as the brain that processes what they see.
Today, image recognition is one of the most widely used applications of computer vision. Pattern recognition in images and feature extraction are also essential parts of other more sophisticated computer vision techniques such as object detection and image segmentation.
A fairly large and versatile recognition feature can provide a number of useful functions for both personal and commercial use, for example:
These are just a few of the options available. The bottom line is that image recognition is already shaping our future.
Leading tech companies have been offering image recognition services for a long time. For example, Amazon has Rekognition (since 2016), and Google has Lens and Cloud Vision (since 2017).
Amazon Rekognition is a SaaS image recognition system that allows you to add automatic photo/video analysis and recognition to your application. It works based on deep learning carried out in two ways: with preliminary data collected by Amazon or by its partners and user-configurable data.
Amazon Rekognition recognizes objects, people, actions, scenes, and text in photos/videos as well as any undesirable content. Once a face image is recognized, it is analysed with high precision. It allows you to search for faces that can be used for detection, analysis, and comparison when checking or counting people as necessary. The system is even able to determine the emotional state of a person by external signs.
For businesses, Amazon Rekognition offers an optional Custom Labels service that can help you identify objects and scenes that are relevant to your business. For example, you can create a model to classify equipment parts or to identify unhealthy animals. Custom Labels build the model themselves, so users don’t need to do any machine learning. They only need to upload photos of objects or scenes, and the service does the rest.
Google Lens is an image recognition application designed to obtain information about identifiable objects. It works based on a visual analysis carried out by a neural network. With deep learning, it improves image recognition techniques and expands the capabilities of the application.
At first, it was a separate application and then it was integrated into the standard Android camera app. If you point your smartphone camera at an object, Google Lens will try to identify the object, read a barcode or QR code, tags or text, and then display search results, web pages, and additional information. Lens is also embedded into the Google Photos and Google Assistant apps. Today, the application can process a photo and translate text or call a number, look for things or furniture in online stores, recognize a menu and recommend listed dishes. Not to mention the identification of landmarks, animals, or plants.
For businesses and developers, Google offers the Cloud Vision API, which makes it easy to integrate image recognition features into their own applications so that they can identify objects in photographs, too. The API service can recognize faces, brand logos, texts — everything that can be used in business. The Lens application uses this Google API for image recognition as well.
People have long and thoroughly tested the work of neural networks for image recognition, mainly in the field of entertainment:
However, image recognition programs are not limited to entertainment functions. Some applications can help people identify what they see. Now users can quickly find information about the desired item on the Internet, for example, its exact name, price, and where to buy it. Applications recognize film and concert posters, logos, brands, barcodes, QR codes, and more.
The technology has opened up many opportunities for marketing and consumer communication. Companies can now easily track opinion leadership, brand mentions in a photo in the absence of text, reviews of products that are not marked with hashtags, and receive user insights. It has become easier for retailers to increase sales, provide better customer service, select suitable products for clients, and monitor a layout in display windows. So, not only users benefit, but also those who work to meet their needs.
There are many ways to apply image recognition in a way that will give your business a leg up in the industry. Such systems will help to study social exchange, improve communication with users, and attract more customers. Implementing them will allow your application to expand its capabilities and go beyond the mobile device. Polygant’s developers are ready to create or integrate a software of any complexity and customise it to your industry.
We have 10 years of experience in machine learning for image recognition. We develop applications and services for customers the same way we do for ourselves. To find out the cost of work and the development time frame for specific tasks, fill out the application form, and we will contact you directly.