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Image Tools
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Unsure which Ximilar visual AI service is right for you? Browse our FAQs for guidance on choosing the best solution for your needs.
Which services does Ximilar provide, and what are the differences between them?
Ximilar provides visual AI services for image processing automation. They are available through App & API, and can be divided into several basic groups:
The principle of Image Recognition services is they process your image and provide some information about it (e.g., image categories, tags, values, or detected objects). These services are grouped into two service categories based on the degree of customization on your side:
The Visual Search services work with your entire image collection and scan it for images that match a given query image to provide you with the most similar or identical images. This is useful for duplicate identification, product recommendations, or finding images and can be used in a combination with image recognition services.
The Image Tools generally take an image and modify it. You can use our tools Background Removal and Image Upscaler on their own, or implement them into more complex systems.
We will help you set up & configure these services so that they fit your needs. Ximilar also provides custom visual AI solutions for specific domains and use cases; in many cases, you can train your own ML models using our Ximilar App platform, or we can prepare the service exactly for your data and use case. Feel free to contact us in case you want to discuss the best option for you.
I want to train my own image-processing AI. Which service should I use?
Our Computer Vision Platform enables you to train primarily Custom Image Recognition tasks (machine learning models). The Custom Image Recognition services are Categorization & Tagging, which automatically recognizes the pictures, categorizes them and provides tags, Image Regression, and Object Detection, which automatically detects objects of given categories in your images. We can also customize our Ready-to-use Image Recognition services to suit your use case and data.
Apart from that, our platform enables the training of Visual Search tasks. Visual Search services are Visual Product Search, Photo Similarity, Fashion Search and Custom Visual Search. However, these services usually require some degree of customization and professional assistance to achieve the desired results.
How Ximilar streamlines image processing tasks and reduces costs?
Ximilar's systems significantly reduce image processing costs by automating repetitive tasks such as analyzing, tagging and sorting of images. This automation results in significant long-term savings, allowing for continuous 24/7 addition of new visual content without additional metadata.
We're continually enhancing our platform, which enables us to both build services efficiently and quickly and also to modify existing solutions to suit your needs. We reduce costs and labour by utilizing a combination of pre-trained and new models.
Service use is billed via API credits, with a customizable monthly plan based on your consumption. Using our credit calculator helps you optimize cost-effectiveness. For sudden system loads, you can add extra credit packs to your monthly credit supply.
Once your solution is live, we can continually upgrade and enhance it, altering any component in the modular structure. Our feedback mechanisms help us understand which model performs best, allowing us to refine the solution. Also, our ready-to-use solutions are routinely updated to stay industry-relevant, and these upgrades come at no extra cost.
What is the difference between Product Similarity, Search by Photo, Photo Similarity, Fashion Search, Home Decor Search and Custom Visual Search?
Product Similarity (visual product search) was built for e-commerce. It is useful for finding similar pictures of products with image queries, similar product recommendations, and product matching.
Search by Photo (product search by image) combines product similarity with object detection to provide similar pictures specifically to the detected object, such as a piece of fashion apparel. It can be used in reverse search engine for fashion, home decor, and other e-commerce product search engines.
Photo Similarity (similar photo search) works with the same technology, but it was trained for generic images, such as stock photos or real-life images.
Fashion Search is a specialized service for fashion e-commerce, which combines visual & similarity search with object detection (Search by Photo) and Fashion Tagging.
Home Decor Search works in the same way in the field of home decor and furniture photos. It also combines visual & similarity search with object detection (Search by Photo) and Furniture & Home Decor Tagging.
Custom Visual Search refers to all solutions using visual & similarity search we build from scratch for our customers.
Can Visual AI help with sorting, filtering, and recommendations of images or products on my website?
Our tagging services recognize objects and attributes in your newly uploaded photos and provide you with tags (keywords). You can either use ready-to-use services Fashion Tagging and Home Decor & Furniture Tagging, train your own Categorization & Tagging tasks, or contact us to discuss a custom solution. The tagging can be combined with Object Detection to tag the detected objects.
Visual Search services are based on the analysis and comparison of the visual appearance of your images, and can therefore be used on their own, typically for recommendations of similar products or products matching the image query, or matching of duplicates. These services are ideal for e-shops, price comparison websites, collectibles’ databases, sellers of antique and design products and so on.
If you work with fashion product photos, you can either use Fashion Tagging, Visual Product Search, or Fashion Search, combining the benefits of these two.
How Visual AI helps e-shops, price comparators, or collectibles’ sites?
Ideal services for e-commerce businesses are usually object detection with a tagging service and visual search. All of them can be combined.
For example, if you upload a photo of a model wearing multiple pieces of clothing and accessories, all products will be detected, and automatically tagged, and then your users will be provided with exact or most similar matches to the detected products.
We already combined Fashion Tagging and Visual Product Search into the service Fashion Search tailored for the fashion industry.
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Which visual AI does visual inspection or quality control in manufacturing?
The typical AI services for visual inspection and quality control are image recognition, which can be used to categorize products on an assembly line, and object detection, which can detect errors, anomalies and faulty items. The image regression provides a numerical value, for example, for a level of wear out of the item. Visual search services such as image matching can help you compare similar images and patterns.
How to find duplicates, enrich product galleries, or add the metadata to the new images?
Visual Search services enable image processing independently of their origin, tags, keywords, and metadata. To add new images to existing items in your collection or enrich newly added images with metadata of existing similar items, use the Visual Product Search for product matching. It is also able to identify duplicate or nearly identical images in your collection.
What is the use of image recognition in retail?
In retail, image recognition is pivotal in optimizing operations depending on visual data processing. One significant application lies in inventory management, where it automates tracking products and stock levels, streamlining restocking processes and minimizing manual effort.
Additionally, image recognition helps with consumer research, enabling retailers to gain insights into customer demographics and behaviour within physical stores. This information aids in optimizing store layouts, product placements, and staffing strategies to enhance the overall shopping experience.
Image recognition also supports personalized marketing initiatives by analyzing customer preferences and purchase history, allowing e-shops to tailor promotions and recommendations accordingly. This personalized shopping experience fosters stronger customer engagement and increases sales.
In many of these applications, image recognition works in tandem with visual search technology, which identifies visually similar products to items detected in product photos and real-life images.
In which fields does image recognition help?
Image recognition technology finds widespread application in diverse fields such as healthcare, retail, and security systems.
In healthcare, it aids in the interpretation of medical images, assisting clinicians in diagnosing diseases and identifying anomalies with greater precision. Read about some of our use cases here.
Similarly, in retail, image recognition streamlines checkout processes, and, together with visual search, enhances customer experience through personalized recommendations.
In security, it strengthens surveillance systems by enabling real-time monitoring, threat detection, and facial recognition.
This technology is also essential for autonomous vehicles, enabling them to perceive their surroundings through cameras and sensors, recognize objects, pedestrians, and road signs, and make real-time decisions for safe navigation.
Additionally, image recognition systems help in both research and applied sciences. For instance, in biological research, microscopy image analysis and wildlife conservation. It plays a crucial role in monitoring and protecting endangered species. It enables researchers and conservationists to analyze vast amounts of camera trap data efficiently, identifying and tracking individual animals, assessing population dynamics, and detecting potential threats such as poaching or habitat loss.
Image recognition aids satellite imagery analysis, especially in monitoring vegetation coverage crucial for sectors like insurance and agriculture. LAICA, by World From Space (WFS) and Ximilar, addresses this, using deep learning to merge satellite data for daily vegetation monitoring despite cloud cover challenges.
In social media, image recognition facilitates image tagging and content moderation.
What is the use of image recognition in healthcare?
Image recognition helps optimize diagnostics, treatment, as well as patient care by employing advanced AI algorithms to rapidly analyze medical imagery. It facilitates early disease detection, personalized treatment plans, and efficient workflows for healthcare providers. Key applications include diagnostic imaging and disease detection, such as analyzing X-ray or microscopy images, as well as providing surgical assistance. Additionally, the technology helps with other vital use cases such as drug discovery and health data analysis.
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