The technology is frequently employed for similarity searches, particularly for product recommendations in e-commerce. It assesses image or detected object features, like colour, edges, or patterns, to suggest the most similar alternatives.

Another typical use is image & product matching. Since the technology can identify duplicates or nearly identical items in various images with different quality, it can help with curating product galleries and eliminating all unnecessary content.

Even though applications in e-commerce are the most common, visual search provides endless possibilities even in the industrial sector, scientific research or security systems.

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1. Similarity Search: Offer users suggestions based on similar items, allowing them to explore alternatives effortlessly, including variations in colours and materials. Learn more about similar product recommendations here.
2. Reverse Image Search: Enable users to search using real-life photos, social media posts, or product images, instantly providing them with desired items or closely related alternatives. Our search by photo first detects all products in an uploaded image and then automatically provides alternatives to each item individually.
3. Personalized Recommendations: Predict users’ preferences by analyzing their uploaded, saved, liked, or purchased items, ensuring tailored recommendations that fit their individual tastes. Our visual search solutions are tailored to analyze the overall aesthetics of the items, including shapes, patterns, logos, colours, or materials.

The post How can visual search help to enhance the user experience on my website? appeared first on Ximilar: Visual AI for Business.

The synchronization frequency is flexible, allowing you to choose the frequency that suits your workflow. During each synchronization run, images are analyzed for relevant features, ensuring efficient visual search, and promptly discarded to uphold privacy and data security.

If you would like to get a visual search solution for your website, app or system, contact us, and we will find the optimal solution for you.

The post How does Ximilar’s visual search implementation via API work? appeared first on Ximilar: Visual AI for Business.

• Visual search together with other visual AI techniques enhances search capabilities of search engines, optimizing content organization and accessibility.
• In retail, it’s transforming the shopping experience by enabling instant product identification and recommendation, leading to increased sales and customer satisfaction. Learn more here.
• In healthcare, visual search aids in swiftly and accurately analyzing diagnostic images and enables efficient retrieval and organization of medical images and records, ultimately improving patient care and outcomes.
• Marketers are leveraging visual search to drive brand visibility and engagement, enhancing advertising strategies and conversion rates. Learn more here.
• Automotive industries are incorporating visual search technologies to advance autonomous driving capabilities, enhancing object recognition and real-time decision-making.
• It also helps with content management and large image collection curation across numerous industries. Learn more here.

The post How does Visual Search help, and in which industries is it utilized? appeared first on Ximilar: Visual AI for Business.

Each image in this collection is processed exactly once during the collection synchronization run, and then discarded. Our AI then works with a representation of your images and compares them for similarity. That is why we do not store your images used for visual search. They are discarded immediately after processing.

The type and source of the images in your collection are up to you, as well as the frequency of the synchronization runs. Contact us via live chat or contact form for details.

The post Do I need my own image collection for visual search? Can it be performed on images from social media? appeared first on Ximilar: Visual AI for Business.

Fashion Search is an all-encompassing solution, wrapping all typical fashion AI services into one. It integrates:

• Fashion tagging, including Dominant colors
• Object detection for precise labelling and search of individual items
• Visual Search, recommending similar items from your collection

Both Fashion Tagging and Fashion Search include color analysis. The colors are supplied as tags and can serve for filtering and search on your website.

I only need a single fashion AI solution

All our fashion AI solutions can also be employed individually. Examples include product similarity, search by photo (reverse image search), fashion apparel detection, or color-based search.

The post What are the differences between Ximilar fashion solutions for recognition, data enrichment and product search? appeared first on Ximilar: Visual AI for Business.

1. Object Detection & Fashion Tagging

Our Fashion Tagging automatically identifies and tags the fashion apparel in your images. It utilizes a hundred recognition models, hundreds of labels, and dozens of features, all linked into seamlessly interconnected flows, enabling you to add content 24/7. We continually enhance the quality and incorporate new fashion attributes like features, categories, and tags. Custom tags are also encouraged.

Standalone fashion tagging assigns tags to a single dominant fashion item in an image. However, when united with object detection, it’s capable of tagging all recognizable fashion apparel in an image.

2. Product Similarity & Search by Photo

These visual search solutions allow hyper-personalization of customer experience. Product Similarity identifies and proposes items similar to the one your customer is currently viewing, leading up to a 380% boost in clicks.

Search by Photo accepts user-uploaded images, detects fashion apparel in them, and automatically suggests similar items from your inventory. This applies to real-life photos, user-generated content, as well as influencer photos and other social media content.

The post What solutions does Fashion Search combine? How does it work? appeared first on Ximilar: Visual AI for Business.

Dominant product vs. whole image

The product endpoint allows you to extract colors from a single dominant object in an image (product photo), whereas the generic endpoint extracts the dominant colors from the entire image, a mode typically used in stock photography.

Basic color for searching & filtering

This mode identifies one main color of the dominant object out of a total of 16 basic colors. The extracted color can be utilized as an attribute for filtering and searching fashion items.

Pantone palette: detailed color analysis

This mode provides a group of dominant colors, their hex codes, the closest Pantone name, and coverage of the image in %. It is ideal for similarity search (search by color).

The post What colors and which palettes can Ximilar AI extract? What is the format of the results? appeared first on Ximilar: Visual AI for Business.

Apparel visibility

To achieve the most accurate results, the fashion apparel you wish to detect should be clearly visible, with minimal overlapping objects or folds, and not worn by individuals in uncommon poses. However, if your collection comprises numerous photos with unconventional positions, we can readily tailor the solution to perfectly suit your use case.

The post Can Ximilar fashion AI identify apparel on people? Is it necessary to supply product photos for object detection? appeared first on Ximilar: Visual AI for Business.

• By default, Fashion Tagging and Search focus on the largest fashion object in the image. This mode is ideal for single-product photos and other images with dominant objects.
• However, you can opt to detect all fashion items using the detection endpoint, then individually tag and use them for separate visual searches.
• The tagging can also be enhanced with a meta endpoint describing the background, scene, view, and body part of the person wearing the items.

The post Which items and how many can Ximilar fashion AI detect in a photo? appeared first on Ximilar: Visual AI for Business.