What Is Image Classification?

Key takeaways

Image classification, an important aspect of computer vision, is a computer’s ability to recognize and classify images.

• Image classification is used in areas such as medical imaging, construction, agriculture, security, and online shopping.

• Image classification methods include those that classify images into distinct categories and those that support multilabeling, allowing objects to belong to more than one category. 

• You can use image classification in a variety of artificial intelligence roles, including AI engineering, algorithmic science, and AI development. 

Explore image classification, including real-world applications of this cutting-edge technology and careers that use image classification. To deepen your understanding of image processing, enroll in the Image Processing for Engineering and Science Specialization, where you can learn about computer vision, spatial analysis, digital signal processing, and more. 

What is image classification in AI?

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. 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 support a computer’s ability to accurately classify images.

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. 

What is image classification in GIS?

Image classification in geographic information systems (GIS) is the process of categorizing pixels in multiband remote sensing imagery into land-cover classes, such as pasture, forest, or urban areas, to convert the data into thematic maps. It uses spectral signatures and computer algorithms to classify features. 

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 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

Image 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 can use image classification to monitor work progress, inform safety policies, and monitor quality. 

• 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 items 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. If you’re interested in image classification, a few careers to explore include AI engineer, algorithm scientist, or AI/ML developer. 

*Glassdoor median total salaries include base salary and additional pay, such as profit-sharing, commissions, bonuses, or other compensation.

1. AI engineer

Median annual salary in the US (Glassdoor): $140,000 [1]

Job outlook (projected growth from 2024 to 2034): 20 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

Median annual salary in the US (Glassdoor): $223,000 [3]

Job outlook (projected growth from 2024 to 2034): 20 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

Median annual salary in the US (Glassdoor): $158,000 [4]

Job outlook (projected growth from 2024 to 2034): 20 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 depending on how 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 more accurate and consistent image analysis in fields where it matters, like medical imaging. 

At the same time, image classification may also present challenges. Compared to a human’s ability to process visual information, the process is typically much more difficult for a computer. 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. For example, an image classifica

tion 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 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. 

Explore free AI and machine learning resources

Join Career Chat on LinkedIn to get timely updates on popular skills, tools, and certifications. Or if you want to learn more about image classification and other areas related to AI technology, check out these free resources:

• Discover trending skills: 5 Fastest-Growing AI Skills to Build

• Watch on YouTube: Support Vector Machine RBF: When Linear Kernels Fail, RBF Succeeds

• Explore career paths: AI Career Quiz: Is It Right for You? Find Your Role

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