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Current scientific and technological development makes computers see and, more importantly, understand objects in space as humans do. In 2021, image recognition is no longer a theory or an idea of science fiction. According to Markets and Markets, this is a fast-developing market, with predicted growth from USD 26.2 billion in 2020 to USD 53.0 billion by 2025, and a CAGR of 15.1 % for the period. Solutions based on image recognition technology already solve different business tasks in healthcare, eCommerce and other industries.
In this article, we will tell you about the principles of how this magic works, see the most popular use cases and clarify how to harness this powerful tool to enhance business activities.
Image recognition in the area of computer vision (CV) and machine learning (ML) is the ability of the computer to understand what is depicted on an image or video frame and identify its class. In a technical context, it’s a simulation of recognition processes executed by the human brain, where math functions serve as surrogates of real neural processes.
People often imply image classification, object localization, and object detection with the image recognition term. This is a popular misconception. Indeed, all of them are isolated tasks on the same nesting level in the context of computer vision. To make it clear, consider each of them by example.
Image recognition in fact is only image classification. As part of this objective, neural networks identify objects in the image and assign them one of the predefined groups or classifications.
Imagine a neural network that knows two classes: cat and dog. If you input an image with a cat or dog, the result of image classification will be that it’s a cat, or it’s a dog. However, if it’s another animal, the algorithm will not recognize what it is.
Object localization is one task of CV. In addition to assigning a class to an object, neural network image processing has to show the recognized object’s contained space by outlining it with a rectangle in the image.
Object detection is one more task, which is based on AI image recognition. It performs image classification and object localization to multiple objects in the input image.
Instead of aligning boxes around the objects, an algorithm identifies all pixels that belong to each class. This method is used to process tasks when precisely identifying the object’s shapes is required, such as image recognition systems for surface segmentation from satellites.
A neural network has to identify instances among objects associated with one class, a dog in our example. As with semantic segmentation, the result is a calculation of pixels belonging to each group representative.
As mentioned before, image recognition technology imitates processes that take place in our heads. Due to the exceptional structure of the human brain, we learn to recognize objects extremely quickly and do not even notice these processes. Our brain is capable of generating neuron impulses subconsciously or automatically in the context of technical language.
Despite all tech innovations, computers can’t boast the same recognition ability as humans. For them, an image is a set of pixels, which, in turn, are described by numerical values representing their characteristics. Neural networks process these values using deep learning algorithms, comparing them with particular threshold parameters. Changing their configuration impacts network behavior and sets rules on how to identify objects.
There are numerous types of neural networks that exist, and each of them is a better fit for specific purposes. Convolutional neural networks (CNN) demonstrate the best results with deep learning image recognition due to their unique principle of work. Currently, several variants of CNN architecture exist. Let’s consider a traditional variant just to understand what is happening under the hood.
Most types of neural network CNN start with an input layer, which serves as an entrance to the neural network. It serves to input starting numerical data into a machine learning algorithm. Depending on the type of input data, they can have different representations: for an RGB image it will be a cube matrix, and for a monochrome image, a square array.
This is where all the magic happens. Hidden CNN layers consist of a convolutional layer, a pooling layer, normalization, and activation function. Let’s see in detail what is happening in each layer of the image recognition algorithm.
Сonvolutional Layer
As mentioned above, the CNN working principle is distinguished from traditional architecture with fully connected layers in which each value is fed to each neuron of the layer. Instead of this, CNN uses trainable filters or kernels, generating feature maps. Depending on the input image, this is a 2D or 3D matrix, whose elements are trainable weights.
The filter is overlaid on the input matrix. Their values are multiplied and summed to get one number. After that, the filter makes a “step,” flipping by a stride length value, and multiplication of elements repeats. The result will be a 2D matrix of the same or smaller size called a feature map or pattern.
Batch Normalization
Generally, normalization is carried out directly before the activation function. In simple words, it’s a specific math function with two trainable parameters: expectation and variance. Its task is to normalize values and equalize them in a particular range convenient for activation function. Normalization’s primary purpose is to reduce training time and increase performance. It also provides the capability of configuring each layer separately, with minimal effect on others.
Activation Function
This is a kind of barrier which doesn’t pass some particular values. Many math functions are used in computer vision algorithms for this purpose. However, the most usual choice for image recognition tasks is rectified linear unit activation function (ReLU). This function checks each array element, and if the value is negative, substitutes it with 0.
Pooling Layer
The neural network doesn’t train here. This layer is used to decrease the input layer’s size by selecting the maximum or average value in the area defined by a kernel. The pooling layer is a vital stage; its absence will lead to output and input being the same dimension, which dramatically increases the number of adjustable parameters, requires much more computer processing, and decreases the algorithm’s entire efficiency.
The iterative process of “convolution-normalization-activation function-pooling-convolution again…” can repeat multiple times, depending on the neural network’s topology. The last feature map is converted into a dimensional array called the flatten layer which will be fed to the output layer. Feature maps generated in the first convolutional layers learn more general patterns, while the last ones learn more specific features.
This layer consists of some neurons, and each of them characterizes one of the algorithm’s classes. Output values are corrected with the softmax function in such a way that their sum begins to equal 1. The biggest value will become the network’s answer, to which the class input image belongs.
World-known names like Google, IBM, Azure, and AWS offer numerous ready-made solutions regarding image recognition and machine learning processing. If your task is broad, such as scanning, translating, or recognizing hand-written text, or identifying plants, animals, or human faces, you can use ready-made neural algorithms provided by these technology giants and adjust them according to your needs. This provides several benefits at once:
Besides ready-made products, there are numerous services, including software environments, frameworks, and libraries that help efficiently build, train and deploy machine learning algorithms. The most well-known TensorFlow from Google, Python-based library Keras, open-source framework Caffe, gaining popularity PyTorch, and Microsoft Cognitive Toolkit providing full integration of Azure services.
In general, it’s possible to create and train a machine learning system with a regular personal computer. However, the lack of computing power will cause the training process to take months. Saving an incredible amount of time is one of the primary reasons why neural networks are deployed in the cloud instead of locally.
In modern realities, deep learning image recognition is a widely-used technology that impacts different business areas and our live aspects. It would be a long list if we named all industries that benefited from machine learning solutions. However, the most compelling use cases in particular business domains have to be highlighted.
Every day, medics worldwide make decisions on which human lives depend. Despite years of experience and practice, doctors can make mistakes like any other person, especially in the case of a large number of patients. Many healthcare facilities have already implemented image recognition technologies to provide experts with AI assistance in numerous medical disciplines. One of the most famous cases is when a deep learning algorithm helps analyze radiology results such as MRI, CT, X-ray. Trained neural networks help doctors find deviations, make more precise diagnoses, and increase the overall efficiency of results processing.
One more example is the AI image recognition platform for boosting reproductive science developed by NIX engineers. This solution helps minimize human bias and allows for individualized data-driven decisions throughout the in vitro fertilization (IVF) process, defining which and when embryos can be transplanted with the highest chance of success based on the patient’s electronic medical record (EMR), genomics, and visual data.
One of the highest use cases of using AI to identify a person by picture finds application in security domains. This includes identification of employees’ personalities, monitoring the territory of the secure facility, and providing access to corporate computers and other resources. Drones equipped with high-resolution cameras can patrol a particular territory, identifying objects appearing in its sight. It also demanded a solution for military purposes and the security of border areas.
Face recognition software is already standard in many devices, and most people use it without paying attention, like face recognition in smartphones. Given all the benefits of implementing this technology and its development speed, it will soon become standard. Many smart home systems, digital personal assistants, and wireless devices use machine learning and particularly image recognition technology.
Modern vehicles are equipped with numerous driver-assistance systems that help to avoid car accidents, prevent loss of control, and many other things that help to drive safely. The most advanced of them uses complex software consisting of numerous sub-systems working in tandem, including image recognition technology. ML algorithms allow the car to perceive the environment in real-time, define cars, pedestrians, road signs, and other objects on the road. In the future, self-driving cars will use more advanced versions of this technology.
eCommerce is one of the fastest-developing industries, which is often among pioneers that use cutting-edge technologies. Image recognition technology is not an exception. One eCommerce trend in 2021 is a visual search based on deep learning algorithms.
More customers want to take photos of things and see where they can purchase them. Similar features users can already find in Google Lens.
The other areas of eCommerce making use of image recognition technology are marketing and advertising. By recognizing brand logos on images in social networks, companies can more precisely identify their target audiences and better understand their personalities, habits, and preferences—this data analysis leads to increasing efficiency of advertising campaigns, raising conversion rates with less expenditure.
Deep learning technologies offer many solutions that can enhance different aspects of the educational process. Currently, online lessons are common, and in these circumstances, teachers can find it difficult to track students’ reactions through their webcams. Neural networks help identify students’ engagements in the process, recognizing their facial expressions or even body language. Such information is useful for teachers to understand when a student is bored, frustrated, or doesn’t understand, and they can enhance learning materials to prevent this in the future. Image recognition can also be used for automated proctoring during exams, handwriting recognition of students’ work, digitization of learning materials, attendance monitoring, and campus security.
Last but not least is the industry that has to work with thousands of images and hours of video—entertainment and media. Image recognition allows significant simplification of photo stock image cataloging, as well as automation of content moderation to prevent the publishing of prohibited content in social networks. Deep learning algorithms also help to identify fake content created using other algorithms.
An example of the implementation of deep learning algorithms, identifying a person by picture, is FaceMe, an AI web platform, also developed by NIX engineers. It helps photographers to sort photos, search images with specific people, and filter images by emotions. Request a FaceMe demo to see how image recognition works in practice.
Image recognition technology is an accessible and potent tool that can empower businesses from various domains. The NIX team hopes that this article gives you a basic understanding of neural networks and deep learning solutions. If you have a question about this topic, feel free to contact us in any convenient way.
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