Image recognition – an accessible application of Artificial Intelligence

While years ago, Artificial Intelligence seemed like a science fiction projection of the future, it has now become part of our day-to-day life, whether we acknowledge it or not. AI is clearly the most trending part of technology, as it offers humans a tool that can deal with their repetitive and robotic tasks, the immense volume of data, or other complex work.

Probably the most accessible application and an important part of Artificial Intelligence is computer vision. This area of AI is the one that enables a bar code scanner to “see” and identify a series of stripes, or smartphones’ Face ID to tell whether the face in front of the camera belongs to the phone owner, or not. Briefly, just as the part of the human brain that processes the information received by the eyes, whenever a device processes visual input, it uses computer vision to understand what it is seeing.

Of all the uses of computer vision, the one that is the most interesting and prolific in terms of technology is AI image recognition, and it is fueling a visual revolution in the digital space. It is part of the larger procedure called “image processing” and involves detecting and identifying particular features that appear in an image, such as colors, people, buildings, logos, places and other variables. Given the versatility of this AI use, it has become more and more popular lately, with companies from various industries using it to ease their activity.

How does image recognition work?

Before understanding why image recognition has become so popular and how it serves a wide array of industries, it is important to understand how it works.

As with any machine learning process, the first stage is gathering and organizing the data. The computer will process the picture as a raster image or vector, so it will first identify the constructs depicting objects and features of the image, and then it will analyze them. After the analysis, the machine will organize the data: thus, the relevant information is extracted and classified, while unnecessary one is left out. Once this is complete, the machine will build a predictive model, so that the user can enjoy (and benefit from) the image recognition feature. All this is possible due to Artificial Intelligence.

Massive amounts of data are used to “teach” computers to identify, efficiently and accurately, what a certain image is depicting. Some of the massive databases contain millions of pictures described using keyword tags, ranging from landscapes to animals, tech, or food. These huge datasets are the basics of system training. For instance, the device will be able to identify “cats” in photos, simply because it knows what cats look like after analyzing many images tagged with the word “cat”.

There are several implementations of image recognition and we will discuss below some of the most frequently used ones:

Facial recognition – this is probably the most popular branch of image recognition since it has been used by mobile phone makers as a feature that identifies the device’s owner and unlocks the phone. Another well-known Through AI, the system maps certain facial features from an image, then compares the data with previous data it has registered, to find a match. The main issue with this is that no system, not even the most advanced ones, can guarantee 100% accuracy. However, technology is always evolving, so soon this may no longer be a problem.

Object recognition – this feature can be easily used by a lot of industries, as it picks out and identifies one or many objects from an uploaded picture or video. One of the most common applications in technology is visual search.

Text detection – this is quite intuitive: the system detects and extracts text from an image. This feature is used by tools in the digital advertising area: if the ad image consists of certain words, the algorithm will automatically eliminate and not publish it.

Pattern recognition – this feature means finding and extracting specific patterns from a given image. This can be colors, facial expressions, or textures and is mainly used by photo editing programs.

Image analysis – using AI to analyze an image has become very popular for fields that use summaries of specific pictures. All objects, such as colors, shapes, items, will be analyzed so that the user can get brief but exact information about certain images.

What are the best uses for image recognition in business?

There are various methods, techniques and tools for image processing with AI. However, a simple classification can be made by method, since there are only two main methods used: analog and digital. The first one involves processing hard copies of images (such as printed pictures), while the latter manipulates digital images, through computer algorithms.

Whether it’s processing printouts or digital files, image recognition is a feature that can be integrated into several software applications that companies from different business domains are using for various purposes. This helps them automate certain processes, not to mention getting insights about their products, competition, campaigns and many others. Here are some of the most popular types of businesses that benefit from image recognition:

Retail and distribution

Retail stores or distribution chains are probably the ones that can benefit most from image recognition. From visual shoppers, which help customers identify in-store the items they are searching for, to price comparison apps, product label recognition tools, or interactive catalogs – all these can help business owners to improve clients’ experience and thus, increase sales. Employees of these companies can also benefit from image recognition, using apps that help them keep track of products at each point of sale, overview market KPIs, or simply automate certain repetitive tasks.

Visual content creators

For creators, in general, copyright is extremely important. That is why, for image agencies or visual content creators, image recognition tools are more than useful. By identifying patterns, trends, or colors, a piece of software can help discover if somebody’s work has been duplicated or used without offering credit to the author.

Social media platforms

Social media is definitely part of our lives, but most people are not familiar with the systems and algorithms it uses. One of the most relevant components of social media platforms is image recognition. Probably the oldest implementation is the facial recognition feature from Facebook, through which the platform suggests who are the people present in a certain picture so that users can tag them. The system is trained to identify facial expressions, objects, food, landscapes or sports, to translate visual content for blind users, but also to recognize offensive images or sensitive content, and thus remove it from the platform.

These are just a few honorable mentions, which are aimed to illustrate the diversity of applications that AI, machine learning and computer vision have and how they can benefit businesses that operate on large libraries of visual content. Computer vision has grown a lot during the past years, due to deep-learning, comprehensive open-source databases, as well as superior programming tools. However, the power of image recognition is still to be developed, since it holds the potential for a wide array of domains.