Artificial intelligence (AI) often sounds like science fiction, but the concept has, above all, great promise for the future. Although the theories behind AI are actually decades old, the technical possibilities for implementing AI have only recently born fruit. For some years now, a specific application of artificial intelligence has been on the rise: automated image recognition.
In e-commerce, in particular, there are many possible applications for intelligent systems. In today’s article you will learn how image recognition AI works and how blackbee uses AI and image recognition.
This is how image recognition by artificial intelligence works
Artificial intelligence is based on self-learning algorithms. Such algorithms continue to evolve as they receive new information about the task at hand. In this way they are constantly improving the way in which these problems are solved. Such a process is called machine learning.
Self-learning algorithms work because they are based on models that are roughly modelled on the human brain. Like human nerve cells, artificial neuronal networks also consist of nodal points (neurons) that are linked at different levels. Within this group of neurons, information is recorded, processed (by positive or negative weighting) and output as a result. Artificial neural networks seem to be particularly promising, as they have many levels and can therefore recognise more complex patterns. The learning processes that such networks can perform are called deep learning.
For a long time, deep learning wasn’t able to imitate the high complexity of pattern recognition in the human brain. It is only due to increased computing power and the large quantity of available digital data were developers able to achieve success in recent years.
These are the perspectives that image recognition has in e-commerce
E-commerce offers a wide range of applications for automated image recognition. It is, however, difficult to see where image recognition software will prevail in the long run. Read on to see possible areas of application.
Product search using image recognition software
Mobile e-commerce and phenomena such as social shopping have become increasingly important in recent years with the triumph of smartphones. This is why it is becoming increasingly important for you as an online retailer to simplify the search function on your web shop and make it more efficient. One way to achieve this goal is to use image classification. Some large online retailers like ebay, ASOS or Zalando have already implemented image classification. In most cases, functions are available that enable customers to take photos of clothing or other objects and receive product suggestions based on these photos. In addition, screenshots, for example of outfits on social media, can be uploaded to the search function in order to display similar objects.
Product categorization through image recognition
Automated image recognition will also be useful for internal processes in the future. For example, online retailers with a large product range can use image recognition software for product categorization. This software recognizes individual elements within the image and names them. Image recognition, among other things, can therefore help to simplify the process of creating new products in a web shop. The machine vision of our partner Microsoft shows how precise artificial intelligences already recognize images. Any photos can be analysed for their content on the Microsoft Azure website. Go ahead and try it out!
Image recognition in market analysis
Image recognition will also play an important role in monitoring the market in the future. At what prices do your competitors sell certain products that you offer as well? In order to answer this question, so-called matching, the identification of several identical products on different web shops or marketplaces, must happen first. Tools for automated competitive analysis usually do this matching by means of text-based hints. Text-based matching, however, often reaches its limits when, for example, products do not have an identification number, or the product description is imprecise.
blackbee’s new image recognition software
Our self-learning blackbee algorithm already delivers an unprecedented match rate of 98.2 percent. However, we have not yet achieved our goal. That’s why we at blackbee are currently working on the prototype of an innovative deep learning algorithm that will use image recognition to make product matching even more precise for you in the future.
Do you have any questions about our blackbee technology? Write us a message – our market analysis experts are there for you.