Computer Vision vs. Machine Learning: What’s the Difference?

Key takeaways

Machine learning trains computers to solve problems and make decisions, while computer vision helps machines interpret the visual world.

• While computer vision trains computer models to identify and classify visual inputs, machine learning trains them to respond to various inputs based on prior learning.

• Computer vision applications range from language translation, self-driving cars, and damage identification, while machine learning applications include streaming recommendations, data analytics, and email automation.

• You can use computer vision for soil monitoring, medical imaging, and task automation, while machine learning helps with fraud detection, customer service, and route mapping.

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What is computer vision?

Computer vision seeks to provide computers with capabilities similar to human vision. It enables computer models to identify, classify, perceive, and interpret visual inputs and, with machine learning, eventually act on them (through decision-making, forecasting, problem-solving, etc.).

Applications of computer vision

Several processes in everyday life, such as language translation, self-driving cars, and damage identification, involve computer vision. You can look below at a more detailed review of the different applications of this AI technology:

• Apps that scan text (from documents, magazines, menus, road signs, and more) and translate it into the user’s native language 

• The ability of self-driving vehicles to identify and respond to other vehicles, lane dividers, road signs, traffic lights, pedestrians, bicyclists, and stray animals

• Visual inspection software that uses computer vision to detect damages in machinery

Advantages of computer vision across industries

Computer vision has positive effects across many walks of life, with a few examples being soil monitoring, medical imaging, and task automation. Consider in more detail how it benefits various industries: 

• Agriculture: If you’re a farmer, drones with computer vision can help you monitor soil and plant conditions for better crop maintenance and growth. 

• Entertainment: Computer vision can detect how you respond to movies by tracking your eye movements and facial expressions.

• Health care: Machines using computer vision may provide more accurate analysis of your X-rays or CT scans than human doctors.

• Law enforcement: Computer vision machines can help identify humans through fingerprint analysis or iris scanning. 

• Manufacturing: With computer vision, assembly machines can often perform monotonous tasks more accurately than humans, making manufacturing processes safer and more efficient. 

Computer vision challenges

Although new technologies offer many benefits, they also present some challenges, and computer vision is no exception. A few challenges of computer vision technology include:

• The requirement of large data sets for training (and the corresponding time and expense involved)

• Problems with accuracy in a system mimicking human vision, as various factors can affect human perception, including lighting, the sense of smell, and the sense of touch

• Continual advancements in technology and corresponding changes in computer hardware and software

What is machine learning?

As a specific branch of artificial intelligence (AI), machine learning (ML) uses algorithms and data to train computer models to learn and perform appropriate actions based on that learning. As a computer vision component, it enables computer models to respond appropriately when scanning, identifying, and classifying (or “seeing”) objects. When a computer model receives machine training, it relies on patterns and inference instead of human instructions to perform tasks. And, like you, the more machines learn, the smarter they get. 

Types of machine learning

Knowing about different types of machine learning, such as supervised and unsupervised, can help you further define the process. Common types of machine learning include:

• Supervised learning: Use of a labeled data set (raw data pre-classified with labels) to determine performance accuracy in a computer model

• Semi-supervised learning: Use of labeled and unlabeled data sets to train the computer model to label data sets independently

• Unsupervised learning: Use of an unlabeled data set to train the computer model to find patterns and make predictions

• Reinforcement learning: Use of a reward or punishment (i.e., a points system) for trial-and-error learning

Applications of machine learning

Machine learning helps improve daily and workplace processes by contributing to streaming recommendations, data analytics, and email automation. Take a look at some typical applications of ML, including:

• Data analytics

• Email automation

• Natural language processing

• Predictive analytics

• Product recommendations

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

Advantages of machine learning across industries

To understand the many advantages of ML, you can explore its use across various industries. Consider these examples:

• Banking and finance: Machine learning algorithms protect financial institutions and their customers and clients by analyzing massive amounts of data and detecting fraudulent transactions as they happen.

• Business: Chatbots can supply immediate information to customers, making a good alternative to human call centers that put you on hold. It can boost customer satisfaction and save companies money.

• Entertainment: Predictive ML algorithms help companies like Amazon analyze customer purchasing and preference data to recommend future purchases. By detecting patterns that humans might not find, computers can predict what you might enjoy.

• Health care: ML-trained computers can detect medical issues that human doctors may miss. These include cancerous tumors, hairline bone fractures, and neurological problems.

• Transportation: Ride-sharing apps provide quick and efficient ML service for rider-driver matching, price setting, traffic and road construction analysis, and route mapping.

Machine learning challenges

As with computer vision, machine learning also presents some potential challenges for users in terms of cost and accuracy. Below, you can review some common challenges of ML in more detail.

• Cost: Machine learning requires expensive hardware and knowledgeable, high-paid staff.

• Biases: Machine learning models can reflect human trainers’ biases.

• Accuracy: Machine learning models trained on insufficient or inaccurate data can produce inaccurate output.

Difference between computer vision vs. machine learning

The key difference between machine learning and computer vision centers on the method. While computer vision trains computer models to identify and classify visual inputs, machine learning trains computer models to respond to various inputs based on prior learning experience. Although different, both technologies have a common goal: to train computers to perceive and process inputs and respond to them appropriately. 

Explore learning tools that fit your goals

Discover fresh insights into your career or learn about trends in your industry by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep learning more about AI technologies like machine learning, check out these free resources:

• Navigate your learning journey: Machine Learning Roadmap: Beginner to Expert 

• Watch on YouTube: Machine Learning in Real Life: From Spotify to Healthcare

• Hear from an expert: 6 Questions With a Google AI Research Director

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