Image recognition is a part of artificial intelligence where we train the system to understand and identify objects just like we use our eyes and brains to interpret and visually analyze objects. This helps us process things faster using AI without any manual intervention.
Thus, once we feed the system, train it and write a particular algorithm as to how it should function, it would help us in doing many daily tasks with ease.
For example, it is not always possible for traffic police to track the license number of cars that are driving very rashly in traffic, but with image recognition, we can feed the system that if a car is being driven rashly then it will capture the license number of that particular car and it will automatically create a speeding ticket based on that.
Image recognition has come a long way since the early developments in the 1950s where it can only be used to identify only lines and edges.
One of the best image recognition platforms today is Google Vision, which has an accuracy of 81.7%, followed by AWS Rekognition, which has an accuracy of 77.7%.
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History of Image Recognition
The concept of Image Recognition was introduced in the mid-1950s. Certain attempts were made then, laying the groundwork for future developments. Back then, the researchers were focused on simple tasks like recognizing edges.
It was not until the 21st century that image recognition was rapidly developed. This breakthrough revolutionized AI and was adopted globally. With the development of GPUs, further development occurred in the field of Image Recognition. Even in the healthcare department, this technology is being used to analyze visual images like X-rays or CT scans, even aiding in the diagnosis of certain diseases.
- Early Development: In the 1950s-1950-1,960s, the term came into existence, and several attempts were made to make significant progress. However, only certain progress was made by day as it could only identify the line’s edges. With its introduction back in the day it laid a foundation for today’s massive success.
- The era of Machine Learning: Between the 1990s and 2000s, there has been a significant turning point in the history of Image Recognition. Mainly after the introduction of machine learning algorithms and Support Vector Machines (SVM) being one of the most notable inventions, there have been a lot of high-tech developments.
- Deep Learning Era: Since the last decade, there has been a massive development in deep learning, and widespread use of convolutional neural networks, which are the most important architecture behind image recognition tasks.
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What is Image Recognition
Image recognition is the process by which a system can identify objects and classify them based on movements, type, etc. It is one of the best inventions that has sped up the inspection process and other applications and acts as the eyes of AI. Thus, it can help speed up tedious work and make it process images quickly rather than manually inspecting them.
How does Image Recognition Work?
Before we move to Image Recognition, we should first understand how a computer recognizes an image. An image is a two-dimensional image processed by the system as pixels in multiple grids and arrays that make up the total resolution of that particular image.
Each pixel is composed of data that corresponds to the color of that particular box, with multiple pixels in particular. As we can see in the picture below, there is a picture of a cat divided into various pixels. Now, each pixel is recognized by its adjacent pixels, and then the AI recognizes the subject present in the image.
Image Recognition is based on various important elements, one of which is machine learning, where the processor analyzes the data, i.e., the image, and concludes it. Multiple images are provided to the neural networks, and based on that, the AI recognizes them. This is quite similar to how our brain processes different things we see. The computer analyzes the data and recognizes it. With the development of Image Recognition various Engines are now implementing this with high efficiency ratings.
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The most notable engines are Google Vision, Microsoft Azure, Amazon Rekognition and IBM Watson.
| Application | Accuracy Percentages: |
| Google Vision | 92.4% |
| Microsoft Azure : | 90.9% |
| AWS Rekognition : | 88.7% |
| IBM Watson : | 69.3% |
Definition with Examples
In today’s world, there are a lot of areas where Image Recognition is being used in day to day life.
- Facial Recognition: In today’s age, image recognition is very popular in social networking. When an image is uploaded to Facebook, we can see that it recommends tagging the image to a person to whom it already resembles. Thus, a data set is already present in its server when it was already tagged in a previous image. Now, when a new image is being uploaded, it finds a match, and it recommends tagging that individual.
- Search By Images: Sometimes, we can see that we have an image, but we do not know the location of that particular image. But how can we search for something on the internet if we need to see the location of it? By image recognition, we can now search by images as well, like Google. We can upload the image of which we are trying to find the location and then it will return results of the same with a variety of results including the name of that particular location.
- Medical Diagnosis: One of the major benefits of image recognition is its application in the medical field. During an X-ray or MRI, the system helps find any abnormalities and allows for detailed inspection that sometimes cannot be identified by the naked eye.
Source: Allianceenligne
Step-by-Step Process of Image Recognition
There are various techniques used in Image Recognition. A data set is fed to the system, which is then trained and ready to provide actions like recognition (images).
- Gathering Of Data Sets: First, several data sets need to be arranged. This includes multiple images, which are identified by their own characteristics. For example, 50 images of ships, dogs, planes, tigers, and humans, 10 of each 5 modules, have been gathered. Now, we mark these images and classify them.
- Feeding The Neutral Network: Once the images are marked, each of these images is fed into the system. Once it is done, the system gets trained as to which images are marked as what. Once the neural network is trained, the algorithm is created. One of the best versions of a neural network is the CNN (Convolutional Neural Network), as it can apply filters, distinguish accurately between the pixels and colours and their shape, and provide detailed reports of all of this without human intervention.
- Analyzing Analysis: Once the first two steps are done, the system is ready to be fed with new images. Based on the previously fed data sets, it can now be analyzed by comparing them with the previous ones and then responding. Example: as said previously, since the system is already fed with data sets of ships, dogs, planes, tigers, and humans, now, if a new image of a ship is being provided to the system, it will compare it with the previous one and will identify the new image as a ship.
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Limitations:
Although image recognition has many advantages, it also has many limitations and challenges.
- Insufficient Data Sets Issues: If a new issue is found, it cannot identify what data set is being fed to the system and sometimes also identifies the dataset as an “error.”
- Complex Computation: The software or algorithm used to recognize datasets is very complex, sometimes requiring professional people, which limits its usage.
- Reliability: In the medical field, relying on the system fully to identify abnormalities may lead to different complexities. Even though there have been so many technical developments there remains a possibility that it may fail to identify certain abnormalities leading to a failure in identifying a disease.
Conclusion
Since the evolution of Image recognition in the mid-1900s, Image recognition has experienced various developments specially post 2010. In the last decade there has been a massive bloom in its technical development.
With future developments, emerging trends, innovations, more data sets, and continuous improvement of AI, Image recognition will be more efficient, and we will be more dependent on it than ever.
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