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.

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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.

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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.

The post How Ximilar streamlines image processing tasks and reduces costs? appeared first on Ximilar: Visual AI for Business.

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.

The post What is the difference between Product Similarity, Search by Photo, Photo Similarity, Fashion Search, Home Decor Search and Custom Visual Search? appeared first on Ximilar: Visual AI for Business.

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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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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.

The post Can Visual AI help with sorting, filtering, and recommendations of images or products on my website? appeared first on Ximilar: Visual AI for Business.

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.

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