Image Classification: 6 Industries & 26 Use Cases You Can Try

We don't know what kind of business you run, but we're 99.9% sure you can implement image classification applications to seize new opportunities.

 

Yes, the application of image recognition technology is advanced, but it's a small novelty since we all use it in our daily lives without even realizing it. 

 

We can bet you’ve benefited from image classification on Facebook and Instagram, which allows you to automatically tag friends in photos and suggest hashtags based on the content of images. Or maybe you use Google to search by image.

 

But, the power of image classification goes far beyond everyday convenience. 

 

This technology can play an essential role in business transformation, no matter the industry:

 

• Retail for automatic classification of goods by category, improving inventory management 
• Healthcare for the classification of potential skin diseases
• Manufacturing to inspect products on assembly lines for defects

 

and many, many more!

 

In this blog post, our experts explain in simple words

 

• how you can use image classification and demonstrate some real-world image classification use cases to give you an idea of how your business can benefit from this technology.

 

Note: All of this is based on our insights gained while creating a comprehensive image classification solution to enhance productivity, ensure safety, and optimize industrial processes.

Key Takeaways

 

• Image classification is the process of teaching a computer to recognize and classify images into different groups. For example, if you show computer pictures of cats and dogs, image classification helps it correctly label each one as a "cat" or "dog." 

 

• The global image recognition market is projected to reach $178.13 billion by 2032.

 

• Image classification is widely adopted in healthcare, automotive, retail, manufacturing, and security sectors.

 

• Businesses should implement image classification today to boost efficiency by automating tasks, which reduces manual effort and costs. It enhances accuracy in data analysis, improves customer experience with personalized recommendations, and offers real-time insights for faster decision-making.

Image Classification in Action: Use Cases and Real-Life Examples

 

Image classification in the retail industry

 

• Product categorization. Automatically organize products by images, making inventory management more effortless.

 

Diesel makes shopping more accessible for its customers by using automated product tagging. With tags like “3/4 sleeves,” “formal,” and “collared,” shoppers can quickly find exactly what they’re looking for.

Source: Dataweave

 

• Visual search. Customers can find products by uploading photos, improving their experience and increasing sales.

 

“The ability to suggest relevant products based on individual preferences is the cornerstone of modern eсommerce. It transforms shopping into a personalized experience, driving engagement and sales."

 

- Oleksandr Hergardt, Head of ML department.

 

For example, Amazon uses image classification in its app's "StyleSnap" feature, which allows users to upload photos of clothing or accessories. The system detects products and offers similar items for purchase.

 

• Shelf monitoring. In stores, image classification checks if products are misplaced or out of stock, helping with restocking.

 

• Fashion analysis. Detects trends in colors, styles, and patterns, allowing retailers to adjust marketing and inventory.

 

• Warehouse management. Automatically sorts products when they arrive, speeding up fulfillment and reducing errors.

Use of image classification in healthcare

 

• Radiology and medical imaging. Hospitals and diagnostic centers use image classification algorithms to analyze X-rays, MRIs, and CT scans. The technology classifies images based on diseases like fractures, tumors, or other abnormalities.

 

• Dermatology. Platforms like SkinVision use image classification to assess skin conditions. Users upload images of skin lesions, and the system classifies them as benign, suspicious, or urgent.

Source: SkinVision 

 

• Pathology. Algorithms classify images of histological slides to identify cancerous cells or other diseases.

 

• Ophthalmology. Companies like EyeNetra are enhancing healthcare with image classification by analyzing retinal images. The system can classify pictures based on conditions like diabetic retinopathy or glaucoma.

Example of the retinal fundus image classification 

 

• Telemedicine. Telehealth platforms integrate image classification to categorize patient-uploaded images, such as rashes or injuries, before consultations.

 

• Nutritional analysis. Health apps like MyFitnessPal use image classification to analyze food photos. Users can upload pictures of their meals, and the app classifies the food items to estimate nutritional content.

Source: MyFitnessPal

 

Applications of image classification in the manufacturing sector

 

• Quality control. Image classification classifies products as defective or acceptable based on visual attributes like color, size, or surface defects.

 

For example, the well-known company Siemens classifies images of its printed circuit boards for defects. Using image classification in its electronics manufacturing plants, it identifies problems such as missing components, bad solder joints, or misalignments.

 

• Defect detection in assembly lines. The system classifies images of parts to identify issues before they proceed to the next assembly stage.

 

• Inventory management. Factories can maintain accurate inventory records and stock replenishment by classifying images of components or raw materials.

 

"Image classification allows factories to keep precise inventory records by automatically identifying components and materials, ensuring timely stock replenishment and preventing shortages."

 

- Oleksandr Hergardt, Head of ML department

 

• Product sorting. Recycling or waste management companies use image classification to sort materials based on type (e.g., plastic, metal, paper).

Use of image classification technology in agriculture

 

• Crop health monitoring. Farmers use image classification to classify crops based on health status. This system classifies field areas such as healthy, under stress, or diseased.

 

For example, Edenworks, an indoor aquaponics company, uses image classification to monitor and manage the plants in its aquaponics systems. By classifying images of plants and identifying their growth stages or potential diseases, Edenworks optimizes its cultivation techniques.

 

• Fruit sorting. Fruit-packing facilities use image classification to automatically sort fruits based on size, ripeness, and quality. For example, the system classifies apples by color and size to determine which ones are ready for market.

 

• Yield estimation. Agritech companies employ image classification to estimate crop yield by analyzing images of plants at various growth stages. The system classifies plant health, growth stage, and other factors to predict harvest size.

 

• Plant species identification. Farmers and researchers classify images of plants to monitor biodiversity, select suitable crop varieties, and track plant growth.

 

Read also: How AI Can Transform Your Business

 

Image classification and automotive industry

 

• Autonomous vehicle object classification. Autonomous vehicle companies use image classification to classify objects detected by onboard cameras. The system classifies images like pedestrians, other vehicles, road signs, or obstacles, helping the car make real-time driving decisions.

 

• Quality control in manufacturing. Automotive manufacturers use image classification on assembly lines to inspect parts for defects.

 

For example, to maintain high-quality standards in Toyota's manufacturing plants, the company uses image classification technology to analyze images of vehicle parts on the assembly line and check for defects like dents, scratches, or incorrect assembly.

 

• Vehicle part sorting. Automotive suppliers use image classification to sort vehicle parts in warehouses automatically. The system classifies parts based on size, type, and model, ensuring they are sent to the correct location for assembly or shipment.

Applications of image classification in real estate

 

• Property condition assessment. Roof.ai leverages image classification to examine property images and evaluate their condition. By sorting images into categories like "good," "fair," or "poor," the platform assists real estate agents and property managers in assessing a property's state. 

 

• Automated property valuation. Zillow uses image classification algorithms to analyze property images and features (like pools, garages, and landscaping) to assist in determining property values.

 

• Energy efficiency analysis. EnergyHub assesses buildings' energy efficiency by analyzing images of insulation types, window styles, and heating systems. This helps them recommend energy-saving improvements to property owners, boosting efficiency and reducing costs.

 

• Real estate marketing. RealScout categorizes and tags property photos in their listings based on features (like outdoor spaces, kitchens, etc.). This classification enables buyers to filter properties more effectively based on their preferences, enhancing the overall home search experience.

 

Learn more: How to Build Teams for AI Projects

 

Although we’ve only looked at a small selection of applications of image classification in different industries, it’s already clear that this technology has incredible potential.

 

To help you see even more benefits, we’ve created a table comparing critical efficiencies, such as cost, time, and resource allocation, between traditional methods and practical applications of image classification.

 

 

 

 

How Onix Can Help: Our Experience and Services

Onix built an advanced image classification solution leveraging the Intel Neural Compute Stick 2 (NCS2) to address challenges faced by industrial enterprises, such as

 

• defect detection
• quality control,
• anomaly recognition.

 

The solution we developed offers real-time image classification and analysis, eliminating delays associated with cloud processing while ensuring data privacy through edge computing.

Image classification solution built by Onix

 

Key business goals achieved:

 

• Real-time image analysis with minimal latency
• Enhanced data privacy by processing images locally
• Reduced infrastructure costs
• Scalable solution for multiple users

 

Ready to implement image classification?

 

Our ML development services include:

 

• ML consulting. Our experts will demonstrate how you can use ML in your industry to meet your concrete needs.
• ML development. ML experts will be with you every step, from planning and testing to launching and keeping things running smoothly.
• Research and technological dev. We assist you in selecting the best-fit technology and devices for your ML product.
• Maintenance and support. This involves upgrading your ML products based on real user feedback, fine-tuning performance, and adapting to meet your evolving needs for lasting success.
• Prototyping and proof of concept. We’ll validate critical features and assess the feasibility of your idea.

 

"An AI PoC acts as a prototype that showcases potential functionalities and uncovers hidden challenges. It’s like a rehearsal before the grand performance, ensuring everything is in place."

 

– Oleksandr Hergardt, Head of ML Department. 

 

• ML integration. Our developers will help you securely connect data sources and select the best-suited ML model for your requirements.

 

Or…

 

FAQ

 

What are the different image classification techniques?

Common image classification techniques include:

 

• Convolutional Neural Networks (CNNs) for analyzing features through multiple layers, transfer learning for fine-tuning pre-trained models, and traditional algorithms like K-Nearest Neighbors (KNN) and Support Vector Machines (SVMs).

 

• Other methods involve decision trees, Bag of Visual Words (BoVW) for feature representation, autoencoders for dimensionality reduction, and ensemble methods that combine models for improved accuracy.

 

Each technique is selected based on the specific classification needs.

 

How can I start leveraging image classification for my business?

To start leveraging image classification, define your objectives, choose an appropriate image classification tool, collect and prepare labeled images, train the model, and integrate it into your existing systems.

 

What industries can benefit the most from image classification?

Industries such as healthcare, retail, automotive, manufacturing, and agriculture have benefited significantly from image classification. These sectors use the technology for tasks like quality control, disease diagnosis, and product categorization.

 

How does image classification help with data extraction?

Image classification automates extracting data from images by accurately identifying and categorizing relevant information, which reduces manual effort, minimizes errors, and speeds up data processing.

 

What challenges should I be aware of when implementing image classification?

Challenges may include data quality and quantity, the complexity of model training, integration with existing systems, and the need for continuous monitoring and updates to maintain accuracy. A clear strategy and an experienced ML team can help mitigate these issues.

 

How can Onix help my business implement image classification?

We can help your business by providing expertise in developing custom image classification solutions tailored to your specific needs. We help you with data collection and preparation and ensure seamless integration into your existing workflows.