How Does Image Recognition Work?
Technology has seen many advancements lately. One of those advancements is the invention of AI (Artificial Intelligence) technology that allows machines and computers to observe situations, act, and work like humans.
Understanding Image Recognition - How does it work, and what are its applications?
However, AI-backed machines can view and analyze the situation more quickly than humans. Moreover, such machines operate significantly quicker compared to humans.
While AI finds its applications in various fields and industries, its subset, computer vision, has opened the door to new opportunities by introducing image recognition.
Computer vision saw acceptance in the early 2010s when a team developed a deep neural network with an error margin going just above 16%.
It should be noted that neural network was initially developed after getting inspiration from neuron cells in the human brain that enable humans to obtain data from vision and identify objects, faces, actions, and character.
On the other hand, a neural network or artificial neural network (ANN) is a series of algorithms that mimic the activity of neurons in the human brain to identify the actions, characters, faces, and objects in images.
Advanced computer vision technology gave rise to the modern image recognition used by us in our day-to-day life without even realizing how it is helping us.
We have gathered valuable details about its working and applications to help you understand how it benefits us.
Further details are given below:
Working of Image Recognition
Image recognition or CBIR algorithm takes the help of AI, Computer Vision, and Deep Learning to deliver results. It uses neural networks commonly known as CNNs (Convolutional Neural Networks) to identify objects from various angles.
These neural networks view images as matrices with various numerical values.
First, neural networks are fed a massive dataset, including billions of images. The AI behind the machine sees all these images as numerical value matrices.
Then, the machine uses the convolutional neural network to recognize images. Finally, this dataset is fed to the network for comparison with the images provided for recognition.
Here is how it works when an image is provided for recognition purposes.
The first task performed by a machine assigned the task of recognizing an image is scanning the entire image. Computer vision is used for this purpose. During this process, the image is optimized for further processes, and any issues that block the vision are resolved.
This process is used to analyze the content featured by the image.
Classification comes after the scanning process. During this process, the convolutional neural networks identify the objects featured in the images.
Once these objects are identified, neural networks classify them into a particular group or category. The data set that backs these neural networks predates these groups or categories.
This process can be termed a continuation of the classification process. However, during this process, classified objects are localized. The localization of objects featured in an image happens when their location is found.
Generally, these objects are localized by annotating a bounding box around those objects. However, other methods like semantic annotation and key point annotation are also used for the same purpose.
After the detection or localization process, tagging is done for improved accuracy. It is pretty similar to classification but ensures accurate results.
An image can come with multiple tags depending on the variety of objects featured in it. For example, an image featuring the scene of a village road may come with tags like trees, birds, vehicles, and humans.
Segmentation is the process that detects various objects featured in an image to the nearest pixel. This process separates all pixels belonging to a particular category and obtains data from these pixels in the form of numerical values.
Once the data is obtained through the segmentation process, the data is processed and viewed while resolving issues like viewpoint variations, deformation, occlusion, and inter-class variations.
Convolutional neural networks apply multiple filters to improve accuracy and use deep learning algorithms to identify objects featured in the image precisely. Layering also happens during this process.
Finally, CNNs come up with the result using multiple technologies and processes. The use of AI, Computer Vision, and Deep Learning helps in the successful recognition of images.
The objects classified and detected may overlap, but the category with the highest value is raised as the main object featured in the image.
Applications of Image Recognition
Now that we have understood the working of image recognition, it is necessary to understand how we use it daily without even recognizing it. So here are a few applications of image recognition in our life.
Reverse Image Search
The most frequent use of image recognition in our life is the image reverse search. We perform searches on search engines to find similar images. For example, if you don't know the specifics of an object, person, or location featured in the image, you can perform a reverse image search to find similar search results.
The process requires you to search by image using any photo finder to find relevant results. Once you tap on the search button, it brings similar images with corresponding URLs to make it easier for you to know more about the image's content.
Many photo search utilities can display search results from multiple databases in one go. Therefore, you can use it to save time and effort that would otherwise go into performing image reverse searches on individual platforms.
This search method is highly useful when looking for ecommerce merchandise online or trying to know more about a person online. The reverse image search method uses CBIR (Content-based Image Retrieval) algorithm for this purpose, derived from image recognition technology.
Modern smartphones use biometric verification to get unlocked. These smartphones require users to show their faces for verification. If the face matches the stored data, the phone gets unlocked; otherwise, you won't be able to unlock it.
However, face detection technology is not limited to smartphones only. Modern database authorities run by the state also use face detection for various purposes.
From making national identification cards to searching for suspects, face detection databases are handy for various applications.
For example, military services also use face detection methods to monitor borders and sensitive areas. It allows them to identify unusual activities and make real-time decisions to avoid uncalled-for problems.
Image recognition finds another important application in manufacturing lines. People were assigned the task of assuring the quality of manufactured goods needed to catch up with the pace of production lines.
Additionally, they need substantial time to fully scan each product coming out of the production line and identify any faulty unit.
On the other hand, machines backed with image recognition can easily identify flawed pieces. Take the example of a drug manufacturing unit.
Packaged pills can be easily assessed by the person standing in front of the packaging machine. However, a machine will quickly spot any piece that lacks the ideal quantity and eliminate it from the belt.
Image recognition has become an essential part of our life. It is helping us in various ways. We can't even recognize its use in our daily life.
The process may look quite complex to you, but it makes our life easier. The use of image recognition has opened the door for various advancements in our life.
We have discussed the working of image recognition in detail. Additionally, we have discussed a few stand-out applications of image recognition to help you understand how it is making our life easier. Hopefully, you will find this article useful.