Image recognition is an information technology created for obtaining, understanding, processing, and analysing photographs from the real world with their further conversion into digital form. Machine learning, knowledge base expansion, data mining, and pattern recognition are involved in this area.
Advances in graphic image recognition have led computers and smartphones to imitate human vision. Improved cameras in modern devices take pictures of very high resolution (above 30 MP). Then, new programs extract the necessary data from them so that a server will carry out image processing and recognition on their basis.
You may not be aware of this, but the human brain is a brilliant recognition machine because it can receive a lot of information from just one picture. Just look at the picture above. If someone asks you what is in it, what would you answer? 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 to achieve such extreme accuracy. However, image recognition technology brings us closer to this.
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. During the convolution process, each piece of the image is multiplied by the convolution matrix by fragments, and the result is summed and written to a similar position in the output image.
These operations do not actually occur on the mobile devices themselves. Any smartphone, even with the most powerful hardware and software, only sends the photo to the server, where it is processed and checked against the database. So the image recognition neural network is deployed on server hardware, not on user devices. It turns out that in such 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 main and widely used tasks in 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 functionality can provide a number of useful functions for both personal and commercial use as follows:
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: on preliminary data collected by Amazon or its partners and on user-configurable data.
Amazon Rekognition recognizes objects, people, actions, scenes, and text on photos/videos as well as detects undesirable content. Once the 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 is 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 will build the model themselves, so users don’t need to do machine learning. They only need to upload photos of objects or scenes, and the service will do the rest.
Google Lens is an image recognition application designed to obtain information about identifiable objects. It works based on the visual analysis carried out by a neural network. Due to 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, then display search results, web pages, and additional information. Lens is also embedded in Google Photos and Google Assistant apps. Today, the application can process a photo and translate text or call a number from it, look for things or furniture in online stores, recognize the menu and recommend dishes in it. Not to mention the identification of landmarks, animals, plants.
For business and developers, Google offers the Cloud Vision API, which makes it easy to integrate image recognition functionality 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. 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 leaders about them, brand mentions in the photo in the absence of text, reviews on their 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 the 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 that will give your business an advantage in its field. 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. Our developers are ready to create or integrate the software of any complexity with adaptation to your area of activity.
We have 8 years of experience in machine learning for image recognition. We develop applications and services for customers the same way we do it 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 immediately contact you.