Automatic image recognition: with AI, machines learn how to see

It is easy for us to recognize and distinguish visual information such as places, objects and people in images. Traditionally, computers have had more difficulty understanding these images. However, with the help of artificial intelligence (AI), deep learning and image recognition software, they can now decode visual information.

How does automatic image recognition work?

The process of image recognition begins with the collection and organization of raw data. Organizing data means categorizing each image and extracting its physical characteristics. Just as humans learn to identify new elements by looking at them and recognizing peculiarities, so do computers, processing the image into a raster or vector in order to analyze it.

This data is fed into the machine learning algorithm. For the intelligence to be able to recognize patterns in this data, it is crucial to collect and organize the data correctly. Often hundreds or thousands of images are needed to train the intelligence. This phase is called machine learning.

Machine learning is an iterative process. The system trains itself using neural networks, which are the key to deep learning and, in a simplified form, mimic the structure of our brain. This artificial brain tries to recognize patterns in the data to decipher what is seen in the images. The algorithm reviews these data sets and learns what an image of a particular object looks like. It performs tasks such as image processing, image classification, object recognition, object segmentation, image coloring, image reconstruction, and image synthesis. After a certain training period, it is determined based on the test data whether the desired results have been achieved.

The final step is to use the fitting model to decode new images with high fidelity. Image recognition algorithms must be written very carefully, as even small anomalies can render the entire model useless.

How is AI used for image recognition?

Perhaps you yourself have tried an online shopping application that allows you to scan objects to see similar items. Then you've already been in touch with AI in terms of image recognition. Still, you may be wondering why AI is taking a leading role in image recognition . In this section we will discuss the answer to this question.

1. Facial recognition

It is easy for us to recognize other people based on their characteristic facial features. But AI is also being trained to read information from faces . Facial recognition systems can now assign faces to individual people and thus determine people's identity. It compares the image with the thousands and millions of images in the deep learning database to find the person. This technology is currently used in smartphones to unlock the device using facial recognition. Some social networks also use this technology to recognize people in the group photo and automatically tag them.

Properly trained AI can even recognize people's feelings from their facial expressions. To do this, many images of people in a given mood must be analyzed using machine learning to recognize common patterns and assign emotions. Such systems could, for example, recognize people with suicidal intentions at train stations and trigger a corresponding alarm. While there are many advantages to using this technology, face recognition and analysis is a profound invasion of privacy. Because it is still under development, misidentifications cannot be ruled out.

2. Object detection

We can use two deep learning techniques to perform object detection. One is to train a model from scratch and the other is used to adapt an already trained deep learning model. Based on these models, we can create many useful object detection applications. This requires a deep understanding of mathematical and machine learning frameworks. Modern object recognition applications include counting people in an event image or capturing products during the manufacturing process. It can also be used to detect dangerous objects in photos such as knives, guns or similar items.

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3. Text recognition

Image recognition systems can be trained with AI to identify text in images. This plays an important role in the digitization of historical documents and books. There is a whole field of research in artificial intelligence known as OCR (Optical Character Recognition). It involves creating algorithms to extract text from images and transform it into an editable and searchable form.

Automatic image recognition applications.

The use of AI for image recognition is revolutionizing all industries, from retail and security to logistics and marketing. In this section we will look at the main applications of automatic image recognition.

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1. Automatic Image Recognition in Visual Search

Visual Search is a new AI-driven technology that allows the user to perform an online search using real-world images as text replacements.

An example of image recognition applications for visual search is Google Lens. For example, point your smartphone at a flower. If you ask the Google Assistant what item you are pointing at, you will not only get an answer, but also suggestions about local florists. Restaurants or cafes are also recognized and more information is displayed, such as rating, address and opening hours.

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2. Automatic image recognition to organize images.

Meanwhile, taking photos and videos has become easy thanks to the use of smartphones. This results in a large number of recorded objects and makes it difficult to search for specific content. AI image recognition technology allows users to classify captured photos and videos into categories that then lead to better accessibility. When content is properly organized, searching and finding specific images and videos is simple. With AI image recognition technology, images are analyzed and summarized by people, places and objects.

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3. Automatic image recognition for content moderation

User-generated content (USG) is the cornerstone of many social media platforms and content-sharing communities. These multi-billion dollar industries thrive on content created and shared by millions of users. Monitoring this content for compliance with community guidelines is a major challenge that cannot be solved manually. AI-powered image recognition helps automate content moderation. By monitoring, rating and categorizing shared content, it ensures that it meets community guidelines and serves the primary purpose of the platform.

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4. Image recognition technology for innovative new applications

Medical diagnostics

Most commonly, medical image recognition is used to diagnose cancer. Various types of cancer can be identified based on AI interpretation of diagnostic X-ray, CT or MRI images. It is even possible to predict diseases such as diabetes or Alzheimer's disease. Research has shown that these diagnoses are made with impressive accuracy. These systems can detect even the smallest deviations in medical images faster and more accurately than doctors.

Insurance

Automatic image recognition can be used in the insurance industry for the independent interpretation and evaluation of damage images. In addition to the analysis of existing damage patterns, a fictitious damage settlement assessment can also be performed. As a result, insurance companies can process a claim in a short period of time and utilize capacities that have been freed up elsewhere.

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E-commerce

E-commerce companies also use automatic image recognition in visual searches, for example, to make it easier for customers to search for specific products . Online stores can offer customers a special service. Instead of initiating a time-consuming search via the search field, a photo of the desired product can be uploaded. The customer is then presented with a multitude of alternatives from the product database at lightning speed.

We already successfully use automatic image recognition in countless areas of our daily lives. Artificial intelligence is also increasingly being used in business software. As are robots in industrial production. We therefore recommend companies to plan the use of AI in business processes in order to remain competitive in the long term.