Image Classification: Applications & Best Practices
Around 1.72 trillion 1 photos are taken every year. Many are used to train digital solutions, such as self-driving systems powered by image recognition and computer vision (CV) technologies. However, all of these images would be useless for such digital solutions without image classification, a process that identifies and categorizes images based on their visual content.
Image classification is one of the cornerstones of computer vision solutions. However, many executives remain unclear about its applications and best practices, limiting the effectiveness of image classification solutions.
To remedy that, we have curated this article to explore what image classification is and how business leaders can leverage it to develop sophisticated image recognition solutions.
What is image classification?
Image classification is a technique that analyzes the content of an image to assign tags or labels. This distributes the images into different classes. The purpose of this process is to explain the contents of the image to a machine digitally. Image classification is combined with image localization to enable an object detection system to find objects in images.
Single-label vs. multi-label classification
Single-label image classification is a traditional image classification problem where each image is associated with only one label or class. For instance, an image of a cat can be labeled as “cat” and nothing else. The classifier’s task is to predict the label for a given image.
On the other hand, multi-label image classification extends the single-label image classification problem, where an image can have multiple labels or classes. For example, an image of a cat playing in a park can be labeled as “cat” and “park”. The task of the classifier, in this case, is to predict all the relevant labels or classes for a given image.
Multi-label image classification is a more complex problem than single-label image classification, since it requires the classifier to identify multiple objects or features in an image and assign them their corresponding labels.
Image classification can be done manually or with automated tools.
Manual image classification
Manual image classification is done by viewing each image and applying a label or category based on the data processor’s judgment of the contents of the image. Like many other manual tasks, manual image classification can also be tedious and prone to errors.
Automated image classification
Image classification can also be automated through advancements in computer vision and machine learning. This is achieved by feeding the model a large number of images with corresponding labels.
Based on this training data, the model can learn to recognize and categorize objects within new images and automatically classify them. However, such tools are implemented through a supervised classification approach where a human classifier is in the loop. Automated data labeling tools usually include image classification features.
Business applications of image classification
1. Autonomous driving systems
Image classification is an essential part of autonomous driving systems. It is used to detect and classify objects in the vehicle’s surroundings, such as other cars, pedestrians, road signs, and traffic lights. The autonomous driving system uses this information to guide the vehicle.
2. Manufacturing
In the manufacturing sector, image classification is used to implement computer vision-enabled automated solutions in the following ways:
2.1. Quality control and defect detection
Image classification algorithms can be used to ensure quality control of finished products or parts on a manufacturing line. For example, in vehicle manufacturing, image classification algorithms can detect cracks, chips, and other imperfections in car parts.
2.2. Sorting and classification
Image classification algorithms can also be used to sort and categorize products based on their characteristics. For instance, in the food and beverage sector, image classification models can scan and sort fruits and vegetables based on their size, shape, and other attributes.
3. Defense
In the defense sector, image classification is widely used for target identification, surveillance, and threat assessment. The models are made to automatically recognize and categorize objects in pictures and videos taken by satellites or unmanned aerial vehicles (UAVs).
4. Retail/e-commerce
Time and clarity are important to online shoppers, and if an online platform falls short in those areas, it leads to higher cart abandonment rates (See figure 1). Image classification can help in reducing this rate. Image classification can automate product data categorization and tagging on online stores and e-commerce websites.
This can improve the customer shopping experience by allowing them to search for products by relevant categories quickly.
Figure 1. Reasons why people abandon their online shopping carts
5. Healthcare
Image classification leverages computer vision technology in healthcare and radiology. Medical images like X-rays, MRIs, and CT scans can be automatically analyzed using image classification. This enables medical personnel to diagnose and treat patients more accurately and efficiently. Medical image annotation tools usually include image classification features.
For instance, pneumonia is a lung disease that is difficult to detect early by traditional diagnosis methods. An image classification algorithm trained on a dataset of chest X-ray images can accurately detect the presence of pneumonia in new X-rays.
X-ray recognition and classification through deep learning:
6. Security
Image classification can also be used to leverage computer vision in the security sector in the following ways:
- Surveillance: It can automatically identify and categorize objects in surveillance footage. This can help security personnel detect and respond to potential threats more quickly.
- Facial recognition: It can also help authorities identify people of interest in crowds. Image classification can help the security sector leverage computer vision technology in areas such as banks, airports, and other crowded places.
Example: Here is how an image classification system categorizes faces in an image:
Then, a computer vision system uses the classified data to identify people in a surveillance system:
Best practices in developing an image classification tool
1. Data collection
Data collection is one of the most important steps in developing an automated image classifier model. Ensuring the quality and diversity of the training data is crucial, as they directly impact the model’s performance. The dataset should be large-scale and representative of the image sample, covering the full range of categories to be classified.
For instance, a company developing an image classification tool for fashion retail will need different variations of clothing images for training.
2. Data preprocessing
Preprocessing the training data can significantly improve the performance of the classification model. This includes:
- Resizing images
- Removing noise
- Augmenting it for more diversity
For instance, a healthcare organization pre-processes X-ray image data to ensure they are consistent in size and position. They might also apply filters to reduce noise and enhance the key features in the images. This will result in more accurate classifications by the automated tool.
3. Model selection
Image classification can be performed using a wide variety of machine learning algorithms. It is important to understand the problem and the pros and cons of each algorithm before choosing the best for the project.
For example, a surveillance company can select a convolutional neural network (CNN) as the model for its image classification tool, as it is well-suited for identifying objects in visual imagery.
4. Hyperparameter tuning
The model’s performance can be significantly affected by parameter tuning. It’s important to tune the hyperparameters to find the best-performing model for the project.
For example, a retail company can improve the image classification model’s accuracy and reduce the amount of time required for training by tuning the:
- learning rate,
- batch size,
- And the number of epochs (Total number of iterations).
To learn more about the different methods and tools of hyperparameter Optimization, check out this article.
Further reading
- Image Annotation: Definition, Importance & Techniques
- 7 Data Transcription Services & Their Selection Criteria
- Broz, M. (2023). “Number of Photos (2023): Statistics, Facts, & Predictions.” Photutorial. Accessed: Feb 08, 2023.
- Mabrouk et al. (2022). “Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks.” Applied Sciences, 12(13), 6448. Accessed: Feb 08, 2023.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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