All data is made up of bits. There are 8 bits in a byte, and 1024 bytes in a kilobyte. After kilobytes come megabytes—which are made up of 1024 kilobytes. You get the gist, right?
AI technologies like Machine Learning, Deep Learning, and Computer Vision can help us leverage automation to structure and organize this data.
In 2025, we expect to collectively generate, record, copy, and process around 175 zettabytes of data. To put this into perspective, one zettabyte is 8,000,000,000,000,000,000,000 bits.
This is a lot of data—and there are no signs of data creation slowing down.
Images—including pictures and videos—account for a major portion of worldwide data generation. To interpret and organize this data, we turn to AI-powered image classification.
Image classification analyzes photos with AI-based Deep Learning models that can identify and recognize a wide variety of criteria—from image contents to the time of day.
In this article, we’re running you through image classification, how it works, and how you can use it to improve your business operations.
What is image classification?
Image classification is the task of classifying and assigning labels to groupings of images or vectors within an image, based on certain criteria. A label can be assigned based on one or more criteria.
Image classification can be:
- single-label
- multi-label
Let’s see what this means.
Single-label classification vs multi-label classification
In single-label classification, each picture has only one label or annotation, as the name implies. As a result, for each image the model sees, it analyzes and categorizes based on one criterion alone.
For example, you could program an AI model to categorize images based on whether they depict daytime or nighttime scenes.
On the other hand, in multi-label classification, images can have multiple labels, with some images containing all of the labels you are using at the same time.
An example of multi-label classification is classifying movie posters, where a movie can be a part of more than one genre.
How image classification works
Here’s how image classification works, step-by-step:
1. Pre-processing: prepping your data
This step improves image data by eliminating undesired deformities and enhancing specific key aspects of the picture so that Computer Vision models can operate with this better data. Essentially, you’re cleaning your data ready for the AI model to process it.
Data cleaning—sometimes called data cleansing—is an important step in preparing your data for training your model, as inaccuracies in data lead to inaccuracies in the image classification model. During the data cleaning, you can expect to:
- Remove duplicates: duplicate data slows down the training process and can lead to your model giving more weight to duplicated data unnecessarily.
- Cut irrelevant data: including irrelevant data will not help train your model for the desired purpose.
- Filter unwanted outliers: some data—whilst technically relevant—isn’t helpful when training your AI model. Data that falls far outside the norm can skew your model’s predictions, so it’s better to just remove it.
- Detect missing data: missing data can cause issues in the training process—during the process of data cleaning, missing data can be identified and updated accordingly.
- Fix structural errors: most Machine Learning techniques are unable to identify mistakes like a human would, meaning every piece of data needs to be accurately organized.
Well-organized data sets you up for success when it comes to training an image classification model—or any AI model for that matter. For example, let’s say you have a set of fashion images. You want to ensure all images are high-quality, well-lit, and there are no duplicates. The pre-processing step is where we make sure all content is relevant and products are clearly visible.
2. Object detection: locating objects within the image set
This is the process of locating an object, which entails segmenting the picture and determining the location of the object.
Using our previous fashion example, the algorithm could locate skirts, blouses, trousers, etc. In this case, the model can be trained to recognize blouses in the upper part of the image and skirts in the lower part.
3. Object recognition and training: labeling located images
Deep Learning algorithms discover patterns in the picture and characteristics that may be unique to a certain label. The model learns from this dataset and becomes more accurate in the future.
In our fashion image set, you could assign tags like midi, short-sleeve, skirt, blouse, t-shirt, etc.
Once you’ve labeled your data, you need to train your AI model. This involves uploading large amounts of data to each of your labels to give the AI model something to learn from. The more training data you upload—the more accurate your model will be in determining the contents of each image.
4. Object classification: your model is ready to classify your images
This is the final step in the process—you’ve built an AI model that classifies fashion images by several different criteria.
The algorithm uses an appropriate classification approach to classify observed items into predetermined classes. It does this by comparing picture patterns to desired patterns. Now, the items you added as tags in the previous step will be recognized by the algorithm on actual pictures.
5. Connecting to an AI workflow
After completing this process, you can now connect your image classifying AI model to an AI workflow. This defines the input—where new data comes from, and output—what happens once the data has been classified. For example, data could come from new stock intake and output could be to add the data to a Google sheet.
What are the key concepts of image classification?
To get a better understanding of how the model gets trained and how image classification works, let’s take a look at some key terms and technologies involved.
We’ll look at each of these in-depth later, but first, let’s start with an overview:
- The process of Machine Learning and whether it is supervised or unsupervised learning depends on the data and its structure.
- To make the most out of the process, you need to have a high-quality dataset for training.
- AI-powered Computer Vision allows machines to mimic human vision and identify objects in photos.
Let’s dive deeper into the key considerations used in the image classification process.
Supervised learning vs unsupervised learning
To train a machine to classify images, you need massive amounts of data. A machine can learn in two ways:
- supervised
- unsupervised
The more prevalent of the two is supervised learning. This is where a person provides the computer with sample data that is labeled with the correct responses. This teaches the computer to recognize correlations and apply the procedures to new data.
Unsupervised learning isn't as common as supervised learning. Unsupervised learning is characterized by untidy, raw data with no human involvement. It doesn’t use training data. Deep Learning is included as part of this type of learning.
Unsupervised learning can, however, uncover insights that humans haven’t yet identified.
Data for image classification
The data provided to the algorithm is crucial in image classification, especially supervised classification.
Your picture dataset feeds your Machine Learning tool—the better the quality of your data, the more accurate your model.
A high-quality training dataset increases the reliability and efficiency of your AI model’s predictions and enables better-informed decision-making.
Computer Vision
Computer Vision is a branch of AI that allows computers and systems to extract useful information from photos, videos, and other visual inputs. AI solutions can then conduct actions or make suggestions based on that data. If Artificial Intelligence allows computers to think, Computer Vision allows them to see, watch, and interpret.
Computer Vision teaches computers to see as humans do—using algorithms instead of a brain. Humans can spot patterns and abnormalities in an image with their bare eyes, while machines need to be trained to do this.
Algorithms for image classification: Machine Learning and Deep Learning
The difference between Deep Learning and Machine Learning isn’t always clear—but it’s important when considering image classification.
Let’s take a look.
Deep Learning vs Machine Learning
Machine Learning helps computers to learn from data by leveraging algorithms that can execute tasks automatically.
Deep Learning is a type of Machine Learning based on a set of algorithms that are patterned like the human brain. This allows unstructured data, such as documents, photos, and text, to be processed.
It’s considered to be a more advanced type of Machine Learning.
Deep Learning makes use of Neural Networks. Data is transmitted between nodes (like neurons in the human brain) using complex, multi-layered neural connections.
Each of these nodes processes the data and relays the findings to the next tier of nodes. As a response, the data undergoes a non-linear modification that becomes progressively abstract.
While it takes a lot of data to train such a system, it can start producing results almost immediately. There isn't much need for human interaction once the algorithms are in place and functioning.
Various kinds of Neural Networks exist depending on how the hidden layers function. For example, Convolutional Neural Networks, or CNNs, are commonly used in Deep Learning image classification.
In this type of Neural Network, the output of the nodes in the hidden layers of CNNs is not always shared with every node in the following layer. It’s especially useful for image processing and object identification algorithms.
Business applications of image classification for you to consider
Now, let’s see how businesses can use image classification to improve their processes.
Use AI-powered image classification to auto-tag images
Traditionally, e-commerce companies do product image tagging manually. It’s a time-consuming process, especially for companies with huge catalogs.
Automatic tagging is a way of organizing and labeling images based on their content using AI algorithms. The tagging process is automated and executed effectively—without the need for human participation—thanks to powerful picture recognition algorithms that analyze and label images.
Read more about tagging Real Estate here.
Use AI-powered image classification for visual quality inspection
Companies can leverage Deep Learning-based Computer Vision technology to automate product quality inspection. For example, during the manufacturing process.
The objective is to reduce human intervention while achieving human-level accuracy or better, as well as optimizing production capacity and labor costs.
Use AI-powered image classification for content moderation
Fake news and online harassment are two major issues when it comes to online social platforms.
It is difficult for these platforms to carefully examine each post and photograph uploaded. AI can step in and do this work for them—using Machine Learning algorithms to automate content moderation and interpret the material that is posted to their websites automatically.
Use AI-powered image classification for visual search
Visual search is another use for image classification, where users use a reference image they’ve snapped or obtained from the internet to search for comparable photographs or items.
One of the most important responsibilities in the security business is played by this new technology. Drones, surveillance cameras, biometric identification, and other security equipment have all been powered by AI. In day-to-day life, Google Lens is a great example of using AI for visual search.
Use AI-powered image classification for media analysis
We’ve previously spoken about using AI for Sentiment Analysis—we can take a similar approach to image classification. Image classifiers can recognize visual brand mentions by searching through photos. This is referred to as visual listening.
Brands can now do social media monitoring more precisely by examining both textual and visual data. They can evaluate their market share within different client categories, for example, by examining the geographic and demographic information of postings.
How to find the right image classification solution for your business
There are a couple of key factors you want to consider before adopting an image classification solution. These considerations help ensure you find an AI solution that enables you to quickly and efficiently categorize images.
When making your decision, consider the following:
Implementation process:
- How easy is it to set it up?
- Does it provide integrations to the tools you’re already using?
Prediction accuracy:
- How accurate are the tool’s predictions?
- Does it learn from its incorrect predictions?
Identification speed:
- How fast does it process a certain number of images?
- How much time does it save in your processes?
Classification effectiveness:
- How accurate are the results?
- Do you need additional human input?
Ongoing development:
- Is the AI solution kept up to date with the fast-paced AI industry?
- Can it scale as your business grows?
Finding your ideal AIaaS solution is no easy task—and there are lots to choose from.
If you’re looking for an easy-to-use AI solution that learns from previous data, get started building your own image classifier with Levity today. Its easy-to-use AI training process and intuitive workflow builder makes harnessing image classification in your business a breeze.
Wrapping up on AI-powered image classification
Many aspects influence the success, efficiency, and quality of your projects, but selecting the right tools is one of the most crucial. The right image classification tool helps you to save time and cut costs while achieving the greatest outcomes.
Whether you’re manufacturing fidget toys or selling vintage clothing, image classification software can help you improve the accuracy and efficiency of your processes. Join a demo today to find out how Levity can help you get one step ahead of the competition.
