What Is Image Classification?

Image classification, or the ability to recognize and sort the images you see, is an important concept for computer vision. If an artificial intelligence algorithm needs to respond to a visual signal, it must be able to “see” and process the data in its visual field. While this is an intuitive process we typically don’t have to think too much about in human vision; it’s more complex for an AI algorithm. Deep neural networks and other models give image classification a deeper ability to detect images and classify what it sees. 

Explore image classification, including real-world applications of this cutting-edge technology and careers that use image classification. 

What is image classification?

Image classification involves identifying objects in an image and classifying them by type or other criteria. Computers and AI use image classification as part of computer vision. 

To classify the objects within an image, an AI algorithm must first analyze the pixels to look for patterns that suggest objects the model has experience or training with. AI accomplishes this by first preprocessing the image by reducing noise, getting an idea of the main object, and normalizing the pixels. The next step is feature extraction, or determining the features of the images based on geometry and the color of the pixels. From there, the algorithm determines which label to assign the features based on its training. 

Image classification techniques and methods

You may use a few types of image classification methods to achieve different classification techniques. You can, for example, choose between a binary or multiclass system, where items are classified into two distinct categories or several different categories. Another option is multilabel classification, which allows objects to belong to more than one category at a time. 

You may encounter some image classification techniques, including neural networks, support vector machines, fuzzy logic, and genetic algorithms. 

• Neural networks: A neural network is a type of algorithm that uses interconnected nodes to understand, interact with, and learn from data in complex ways. 

• Support vector machines: A support vector machine is an AI model that plots data and finds the line or point that best separates categories. 

• Fuzzy logic: Fuzzy logic is an AI model that can work under a certain level of uncertainty and unpredictable behavior. 

• Genetic algorithm: A genetic algorithm is a model that can create iterations of a program and use genetic principles to improve over time. 

Applications of image classification

Imagine classification has real-world applications you can use or take advantage of in many different ways. Consider how image classification helps medical imaging, online shopping, construction, and self-driving cars. 

• Medical imaging: Image classification can help professionals analyze medical images like X-rays or MRIs to accurately and quickly diagnose potential problems. Image classification can recognize patterns at the pixel level that may be difficult for doctors to see. 

• Improved search and online shopping: You can use image classification to improve your search results when looking for a specific item for sale online. Image classification helps the search algorithm understand whether you want to look at blue or green curtains or cotton versus cashmere socks.

• Construction: On a construction site, project managers and general foremen could use image classification to monitor work progress, inform safety policies, and monitor quality control. 

• Searchable Google Photos: Google Photos offers a feature that allows you to search within the photos you’ve taken. For example, you can search for a keyword like “Christmas,” and the app will return images you’ve taken that include Christmas decorations or other clues that may fall under the category. 

• Self-driving cars: Self-driving cars use computer vision and image classification to understand the route, obstacles, and potential hazards to maneuver safely down the road. For this technology to work, image classification, computer vision, and equipment like cameras all need to work together quickly in real-time. 

• Agriculture: Farmers and other agriculture professionals can use image classification to monitor the health and growth of their crops, predict yield, and sense potential problems like disease or insect damage. 

• Security: Image classification like facial recognition and anomaly detection can help security professionals better monitor who enters and leaves the buildings they protect and receive an alert when something unusual happens. 

Who uses image classification?

Many different professionals in computer science or artificial intelligence use image classification. A few careers to explore include AI engineer, algorithm scientist, or AI/ML developer. 

1. AI engineer

Average annual salary in the US (Glassdoor): $131,924 [1]

Job outlook (projected growth from 2023 to 2033): 26 percent [2]

As an AI engineer, you will create, develop, and train artificial intelligence algorithms and models. You will often not write code yourself but rather API calls or embed code to add AI functionality to your projects. 

2. Algorithm scientist

Average annual salary in the US (Glassdoor): $118,107 [3]

Job outlook (projected growth from 2023 to 2033): 26 percent [2]

As an algorithm scientist, you will write algorithms to solve problems using artificial intelligence. After writing an algorithm, you will run tests and measure how well your algorithms work, making adjustments when needed. 

Read more; AI vs. Algorithm: What’s the Difference?

3. AI/ML developer

Average annual salary in the US (Glassdoor): $105,889 [4]

Job outlook (projected growth from 2023 to 2033): 26 percent [2]

As an artificial intelligence or machine learning developer, you will integrate AI or ML technology into an IT project, such as an application or a website. In this role, you may explain how AI systems work, train team members on how to use AI or ML algorithms and develop data architectures. 

Pros and cons of image classification

The advantages of image classification may appear in myriad ways professionals in different industries use this technology. Image classification speeds up data processing and automates work that once had to be manually entered. Image classification offers a more accurate and consistent analytical ability in fields where accurate image analysis matters, like medical imaging. 

At the same time, image classification may also present challenges. The process is typically much more difficult for a computer to understand the objects it sees than for a human. Low-quality images and complicated scenes may confuse an image classification algorithm. Another challenge to overcome when working with image classification algorithms is that they may require a large data set for training. In other words, an image classification algorithm has to see many images of different cats to accurately classify images of cats, which requires a lot of data labeling. 

Researchers and data scientists are overcoming these challenges with technological advancements, such as more powerful cameras and sensors and using automated data labeling to decrease training time. 

How to start a career in image classification

Although you may explore more than one path into a career as an AI engineer working with image classification, many begin by pursuing a bachelor’s degree. You will need experience with programming languages, statistics, probability linear algebra, and machine learning models like Gaussian mixture and Naive Bayes models. Depending on your career goals, a graduate degree may help you gain advanced skills and help you qualify for more advanced roles. 

Exploring image classification on Coursera

Image classification is a versatile technology that empowers various professionals, from doctors to project managers to security professionals, to work more accurately and efficiently. If you want to take the next step and learn more about image classification, consider the Image Processing for Engineering and Science Specialization offered by MathWorks on Coursera. This program is designed for beginners and covers topics like introduction to image processing, segmentation, filtering, and more. Upon completion, you will gain a shareable certificate to include in your resume, CV, or LinkedIn profile.