What is the image recognition and why is image recognition important? The arrival of artificial intelligence in the IT environment paves the way to new development possibilities for businesses and industries. The number of companies using computer vision and image recognition continues to increase. The system improves business processes and increases productivity. But how does image recognition figure in this?
Image recognition is a subcategory of artificial intelligence and computer vision. The method is a set of techniques to detect and analyze images to help in task automation.
Why is image recognition important and what is involved in image recognition?
The process of image recognition includes data annotation, a specialist service that can be outsourced. In addition, some IT companies, like Dataloop.ai, develop various tools for visual recognition technologies for multiple industries.
You can do video or photo recognition with different degrees of accuracy according to the type of concept or information the client requires. For example, an algorithm or model can detect a particular element or be included in a more significant category.
Image recognition can perform various tasks, including:
- Classification. Here, an image is identified to belong to a class or category. Note that one image can only belong to one class.
- Tagging. This is another classification task, but the degree of accuracy is higher. In tagging, the process can recognize many objects or concepts within the image. Thus, the annotator can assign one or more tags to an image they use.
- Detection. This is essential when the annotator needs to locate a specific object within the image. The item will be given a bounding box when it is located.
- Segmentation. Thisfalls under detection tasks. In segmentation, the annotator finds an element on an image to the nearest pixel, meaning it requires detailed work. Segmentation separates each object from the other things nearby.
How image recognition works
- Technical definition. Image recognition is a set of automated learning technologies and techniques according to an artificial neural network, a mathematical function consisting of data input, parameters, and output. Each network has several neuron layers, which affect each other. The complexity of the structure and architecture of an artificial neural network will depend on the required information. The neural network creates the algorithm that allows a machine to recognize a feature or concept in an image.
- In practice. The process of image recognition is more detailed in practice. The neural networks can recognize different concepts in an image because of the training they receive. First, they are fed sets of carefully selected visual data. Next, the datasets are annotated, using different types of labels that fit the task.
The machine training can only start once the annotation of the entire collection of data sets is complete. The objective of the training is for the algorithm to make predictions after it analyzes an image, meaning that it can assign a class to a specific image or determine the presence of a particular element in the image.
Image recognition hurdles
Given the complexity of image recognition and how exacting the discipline is, it is not surprising that training computers in the context of machine vision are pretty challenging.
- Variation in viewpoint. One of the biggest challenges of the system is the alignment of the image. In real-life cases, the objects in a photo or video clip may be facing in various directions. When these image types are fed into the system, inaccurate values are often predicted, as the machine fails to understand the changes in the objects’ alignment.
- Variance in scale. The differences in size primarily affect the objects’ classification within an image. For example, when you come closer to an object, it appears larger. When you are far from the object, the dimension changes, it becomes smaller. With machines, the variance can lead to inaccurate predictions.
- Deformation of objects. In real-life, objects do not change even if there is some deformity. As the machine learns from the images inputted into it, the system bases its analysis on the specific shape of a particular object. Unlike humans, the machine model will not automatically predict that the object remains the same despite some changes in the form, resulting in inaccuracy.
- Variation within a class. Some objects that belong to the same class have differences in shapes, colors, and sizes. For example, bottles may be big or small, tall, slim, round, square, clear, blue, green, amber. These different attributes of an object within a class can alter the machine’s analysis.
- Blockage. Within an image, some items may block the full view of another object, resulting in a piece of incomplete information supplied to the system.
As you can see, as machine learning expands, it will require more algorithms and a more comprehensive compilation of sample data. The training process for machine learning branches out into more categories, levels, and sub-levels for single and multiple classes.
Applications of image recognition
Image recognition applies to many industries. It can be used as a quality control automation solution for telecommunications. It is applied to intelligent video surveillance systems. Image recognition powers biometric access through face detection.
Even the best surgeons can make a mistake. Healthcare is an industry that needs accuracy. With the assistance of AI technology, healthcare facilities are implementing image recognition systems to assist practitioners. The most popular use cases are X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), with the more modern versions making the radiology results more accurate.
Even if the manufacturing processes in production lines are automatic, they require daily evaluation. Image recognition identifies the quality of the final product to minimize defects.
Machine learning assists customers in visual searches to find the places where they can purchase specific items. In addition, the industry uses image recognition technology to identify logos and brands appearing on social media platforms, which can help companies identify target audiences accurately and efficiently understand their preferences, habits, and personality.
The system helps enhance augmented reality gaming, assist students with learning disabilities, optimize medical imagery, and improve iris recognition. In addition, it’s vital to the development and production of autonomous vehicles.
Self-driving vehicles use image recognition to analyze the activities occurring on the road so that the system can take proper actions. Most vehicles today are already equipped with the various driver-assistance system. They help drivers prevent loss of control and avoid accidents. New algorithms allow cars to recognize multiple objects on the road, road signs and assess the real-time environment.
In today’s AI and digital universe, you can see how vital image recognition is in its numerous applications that benefit workers and industries. Hope now its clear to you that why is image recognition important.