Computer Vision vs. Machine Learning | How Do They Relate?

Wondering about computer vision vs. machine learning? We explain what they are, how they work, and how they relate to each other.

What are the key differences between machine learning and computer vision?

Computer vision is a subset of machine learning that enables computers to gain a high level of understanding based on videos and digital images.

What Is Machine Learning?

Machine learning (ML) is the development of algorithms and associated systems that can learn behavior strategies within specific environments through instructions and training data sets.

As a subset of artificial intelligence, machine learning largely eschews some of the largest and more philosophical AI concerns to emphasize approaches to learning and training that can create effective machines for any given environment. This discipline focuses on statistical models, algorithms, and learning techniques that can shape machines in industries as diverse as manufacturing, retail, supply chain logistics, food production, and construction.

Several approaches to ML emphasize training algorithms to recognize patterns in data to inform strategic actions in similar environments. These approaches include the following:

  • Supervised Learning: Under supervised learning models, data scientists provide training data sets to ML systems with a directory of inputs and associated desired outputs. Under this approach, the machine learning system can understand the intended results of a given set of actions and build optimal strategies around getting those results.
  • Unsupervised Learning: As is easily inferred from the name, unsupervised learning approaches use unstructured data sets without associated, ideal outputs. It is then up to the machine learning system to understand patterns in the data sets to develop strategies for behavior.
  • Reinforcement Learning: Reinforcement learning, typically used to train independent machine agents in a given system, uses models of cumulative rewards to train agents how to act in different systems. This application of ML is used in multiple industries, but there has been significant research in this area in online multiplayer games.
  • Deep Learning and Neural Networks: Traditionally, machine learning and AI systems used linear or iterative approaches to machine learning. In the 1980s onward, researchers developed “neural network” brains utilizing node-cluster structures and weighted decision-making strategies. This way, machine learning systems could break down complex problems into simpler ones, and the results of more straightforward problems could come together as a more comprehensive solution for larger ones.

Deep learning took this one step further by creating layer-based neural networks where levels of solution-based networks could essentially create an emergent problem-solving engine. For example, a deep-learning brain could have layers where simple pattern-recognition approaches could come together to power complex tasks like facial recognition in images.

Across all of these and other approaches to machine learning, the emphasis is always on how to teach machine learning systems, simulate training environments for machine learning, and use machine learning to power comprehensive artificial intelligence and autonomous systems.

What Is Computer Vision?

When developing artificial intelligence or machine learning, it’s often helpful for data scientists to limit the applicability of those systems. For example, instead of expecting a system to learn and operate across multiple environments, data scientists may instead simply develop machine learning to operate a specific supply chain or manufacturing process.

However, the ultimate goal of MLand artificial intelligence for many researchers is to increase the usefulness of these systems across multiple domains. Even in the earliest days of artificial intelligence, scientists envisioned all-purpose or general artificial intelligence that could power all sorts of systems. Following this, there was a push in AI research through the 1970s and early 1980s to find ways to develop artificial intelligence in areas like image processing, language recognition, and robotics.

The challenge for artificial intelligence in disciplines like robotics is that these systems need to ingest, interpret, and respond to visual information. As such, advancements in areas like advanced visual and acoustic sensors, environmental navigation systems and mobility capabilities sought to keep up with the time.

As artificial intelligence and machine learning have resurged in recent years, thanks to high-performance computing, big data, and cloud systems, so too has the demand for hardware that can support visual data collection for machine learning applications.

Enter computer vision. Computer vision is an application of machine learning and artificial intelligence that takes information from digital images and videos and makes meaningful decisions based on that information.

Like most machine learning systems, computer vision requires significant amounts of data to train algorithms to interpret this data.

Computer vision generally uses two different technologies:

  1. Deep Learning: As mentioned previously, deep learning can support complex problem-solving. More importantly, deep learning utilizing neural networks can essentially train machine “brains” to take in visual data and retain the knowledge of patterns, strategies, and changes to environmental variables over time.
  2. Convolutional Neural Networks: CNNs take visual information like images and break it down into pixels, utilizing “convolutions” (the operation of creating a mathematical function from two other functions) to make predictions about that data.

Essentially, computer vision uses CNNs and deep learning to perform high-speed, high-volume unsupervised learning on visual information to train machine learning systems to interpret data in a way somewhat resembling how a human eye works.

How Does Machine Learning Encompass Computer Vision?

Computer vision is a subset of machine learning. After interest in artificial intelligence and machine learning research waned in the mid-1980s to the mid-1990s, much of the development in the field fragmented into subfields like natural language processing, image recognition, and robotics.

Furthermore, computer vision could be defined as a subset of deep learning. Instead of processing simulated data or statistics, however, computer vision breaks down and interprets visual information.

Significantly, computer vision isn’t necessary in many applications of machine learning. A machine learning system managing a manufacturing line or modeling digital twins for shipping tankers doesn’t have much use for computer vision capabilities. The information these systems need to learn and operate are available as numerical representations.

On the other hand, computer vision systems require visual information to learn and function. Computer vision systems will combine the machine learning approaches previously discussed with hardware like cameras, optical sensors, etc.. This approach does provide some limitations, including challenges with hardware and how to convert images into helpful data structures for machine learning.

Despite these challenges, computer vision has been implemented in several contexts:

  • Self-Driving Cars, where car machine learning systems must collect visual data from piloting the vehicle safely.
  • Retail and Inventory, where advanced cameras in Amazon Go stores have been used to trace when physical items are removed or replaced from shelves to update online inventories while streamlining the checkout process.
  • Healthcare, where images of blood on surgical tools can be used to estimate blood loss and provide accurate information regarding patient condition to surgeons.

Additional Helpful Resources

GPU for AI,ML, and Deep learning
Storage for AI/ML Workloads