How machines learn to see - methods for image recognition

from | 25 September 2018 | Basics

Thanks to new intelligent image recognition analysis techniques, visual information that is an enormous source of untapped knowledge can be made accessible. This makes it possible to implement numerous use cases based on methods such as "computer vision" or "image recognition". There is one crucial challenge here: it is not always easy for an algorithm to tell what exactly can be seen in an image. At first glance, it is sometimes even difficult for humans to tell what can be seen in certain images, as the following example shows.

Even for humans, it is difficult to distinguish at first glance whether it is a muffin or a Chihuahua.

Image recognition

Intelligent software for image recognition must therefore be trained. In the process, adaptive Algorithms the images into small components and look for patterns in the data. Once image recognition programmes are trained, they do their job much faster and much more accurately than humans. However, since this learning phase is associated with a certain amount of effort, a concrete question be present and the economic benefits be ensured.

Link Tip: Are you looking for inspiration for possible Machine Learning use cases? Here we present five use cases for Industry 4.0.

Image data contain a true treasure trove of knowledge

The most original and widespread form of data are Text data. Storing and evaluating these is now standard everywhere. Image data In comparison, the focus of the international media has only been on the Data analyses. In recent years, data archives based on image data, or at least containing large amounts of image data, have grown extremely quickly. The current task is to make this treasure trove of knowledge available to companies.

Visual analysis methods are one of the most interesting ways to obtain information quickly and effectively. In particular, file formats such as PDFs and various image formats up to and including live video contain important information that can be accessed using new image recognition methods. One of the currently best-known areas of application for image recognition processes is certainly the Face recognition for unlocking smartphones.

The basics of image recognition

In the development of software and algorithms for image recognition, the Artificial intelligence or machine learning plays a central role. Teaching adaptive programmes what can be seen in images is definitely associated with challenges. During the learning process, the algorithms have to compare millions of images with each other in order to recognise similar patterns in the images. A time-consuming and resource-intensive process. Especially the next step - matching patterns and abstract concepts and terms - is the most difficult.

This can be shown with a classic example from philosophy lessons, which, however, does have concrete relevance, for example, in the field of commerce or e-commerce. If we try to teach an algorithm to recognise tables in pictures, it has to be able to recognise the tables in the picture. Learning process give a definition of the image content "table". Such a definition could be: An object with a large surface and four legs. But a table can also have three legs or just one. And a chair can also look like a table - to "understand" all these differences, the algorithm needs feedback.

Image recognition in practice: Use Case "Hail Damage

For one of our clients from the insurance industry, we have realised a project in which we used image recognition to Reduce time spent assessing damage by 75 % could.

The challenge was to assess damage caused by natural disasters quickly and effectively. Because it is precisely in these cases that customers should be helped promptly. The example of hail damage illustrates how this can be realised by using systems with image recognition.

Until now, case workers had to travel to the affected regions to assess the damage in person. A very time-consuming process. We have therefore developed a system that allows us to investigate individual regions after a storm, in which Satellite images be evaluated. For this purpose, an algorithm had to be trained to distinguish houses with intact roofs from those with non-intact roofs.

When a claim is reported by insured persons from a certain region, the insurer has a sufficient data basis to assess a case promptly. This makes it possible to repair damage much more quickly. The settlement of claims is carried out by several months to a few days reduced. There are also further advantages in long-term use:

  1. The Satisfaction of customers grows because claims are settled quickly.
  2. The insurer receives a very accurate data basis for improved, future Risk assessment.

How to use the potential of visual information

The improved image recognition processes allow for increased evaluation of image data, especially in the age of artificial intelligence. Visual information is extremely rich, is available very quickly and has the advantage that it is usually available in very large quantities (Big Data). This makes it possible to tap into an enormous component of data that for many years remained excluded from data analyses because it was difficult to effectively extract the information it contained. Today such Image recognition method available, however, and their use economical, opening up a wide range of new Fields of application opened:

  • Object detection and tracking
  • Position and location detection
  • Size and shape measurement
  • Surface inspection
  • Pattern recognition
  • Character recognition
  • Barcode recognition
  • Completeness or level check
  • Traffic control and steering
  • Person recognition

Many processes that are based wholly or partly on visual information can be optimised or completely redesigned using new image recognition methods. Since the information involved is in part very complex, both the Gain knowledge as well as the economic benefits enormous.


Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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