Mobile Recognition Going Mobile: The Future of Mobile Shopping Mobile is critical to retail in the coming years. Forbes said, “87% of connected devices sales by 2017 will be tablets and smartphones.” Mobile is disrupting commerce. Physical shop real estate is shrinking, so e-commerce is more important than ever. Only 4% of budgets go into mobile but it’s where 20% of consumers time is. 8 in 10 smartphone users will use their smartphone at some point during the purchase process. Nowadays appr. 29% of all online purchases are made via mobile devices. Mobile is where the growth is and where it will continue to be. But brands aren’t moving as fast as consumers. Sources: London e-commerce expo 2014-15; Forbes; MobPartner; Mobile Future Institute; Amazon research. The number of purchases made with mobile recognition doubles every two months. In 2015 more retailers will realize the benefits and advantages of the visual recognition technology for both consumers and their respective businesses. Going Mobile: Image Recognition Technology Image recognition for mCommerce and eCommerce is about delivering instant access to a desired product for shoppers, and increasing click through rates, customer engagement and average basket size for retailers. It's just as easy, as using a camera on a mobile device. All a consumer has to do is to take a picture of any product they liked with a mobile gadget. Image recognition application will then automatically find identical or most similar offers on a retailer's catalog. Image recognition application lets users easily shop items directly off the image, without having to scan bar codes, look up links or search for product online or in store. Image recognition helps to capitalize on the smartphone/tablet’s potential to enable consumers make impulse purchases. Studies show that the idea which drives the impulse purchase (you take a stroll and see astonishingly! looking pumps on a lady passing by) remains in the consumer’s brain for only a couple of seconds. Mobile image recognition technology offers the user the freedom to satisfy the impulse wherever he or she is, otherwise they will forget it very quickly. Going Mobile: Kuznech Mobile Recognition Since people are moving more and more into mobile in their way of online search, mobile visual search has become one of big targets for most companies that concentrate on computer vision. Kuznech Mobile Product Recognition is a technological breakthrough for eCommerce and mCommerce. The system automatically recognizes products in a view of a mobile camera, determining their type and finding the same/similar products in a retailer’s catalog. Use a mobile camera to simplify purchases of almost any kind of product — from apparel and accessories to food and home appliances. Kuznech's technology has modeled the human visual cortex in its propensity to pick out many points of interest of the object: it does not scan objects from top to bottom or from one side to another, but creates a "digital fingerprint" (see slide: Vocabulary) of an object. Thanks to this, Kuznech's mobile recognition technology works so quickly and is so easy to use! Now your customer can use a mobile camera to simplify purchases of almost any kind of product — from apparel and accessories to food and home appliances. Kuznech Mobile Recognition: How It Works of de si ER the UM on NS CO the Mobile Recognition by Kuznech simplifies online purchasing to only three steps: Smartphone – Recognition – Purchase. A user just points the mobile camera at a physical object (any product), and it will be instantly recognized by Kuznech’s mobile API. 1 The system processes the query, recognizes the identical items or picks the most similar product out of the retailer’s database. 2 One-two-three, the consumer makes a purchase directly from the gadget wherever they may be. 3 Kuznech Mobile Recognition: How It Works of de si ER the IL on TA RE the You integrate Kuznech’s Mobile Product Recognition API into your current application (or Kuznech can develop an app for you on demand). Mobile Recognition by Kuznech simplifies online purchasing to only three steps: Smartphone – Recognition – Purchase. Kuznech technology processes image input, analyzes it by interest points, and creates a fingerprint of the detected product that is then transferred to the database. The fingerprint of the source product is compared with the fingerprints of other products in your database, and the most similar product is picked. This product page appears on the screen, and a purchase can be made immediately. 1 2 3 4 Kuznech Mobile Recognition: Inside the Technology Kuznech uses a modern visual-recognition technology to detect and identify products on snapshots. To do visual search and recognition, we apply a wide scale of methods: hashing, deep convolutional neural networks (CNN), deep learning, clustering and classification (see slide: Vocabulary). WHAT'S INSIDE KUZNECH TECHNOLOGY: - On images taken by a mobile camera, Kuznech Сlassifier recognizes most popular product categories and detects a product. - Enhanced algorithms quickly separate the detected object from a background. To do visual search and recognition, Kuznech applies a wide scale of methods: hashing, CNN, deep learning, clustering and classification. - Kuznech Core Engine creates a fingerprint of the detected item, performs visual search and compares the recognized item towards other products in the retailer's database. - Kuznech SimilarSearch Engine picks out most similar items and sorts them according to relevance scale. Kuznech Mobile Recognition: Features High upload & response speed. Our engineers effectively combined hand-crafted methods with self-organized structures to provide fast and accurate search and model comparison. Thanks to this, the image’s fingerprint is ultra small, about only 2-5 kb, which is crucial for data transfer in 3G networks. Say NO! to wasting mobile traffic on a large digital fingerprint and YES! to high upload and response speed. Advanced detection and search technologies allow us to provide services, which do not require active user involvement (for example, manual category predefinition or target localization) in the search pipeline. We combined hand-crafted methods with self-organized structures to provide fast and accurate mobile recognition service. Robustness to technical challenges: Kuznech Mobile Product Recognition Technology can effectively deal with technical challenges, typical for mobile image search, like: different quality cameras, shifted color balance, blurring, and over-exposure. Scalability: Kuznech API libraries are built on JAVA/C++. The app can be created for any of the leading platforms: iOS, Android, or WindowsMobile. Kuznech Mobile Recognition: Your Benefits Connect the physical world of the customer with the digital world of your e-store: capitalize on mobile devices’ potential to help consumers make impulse purchases wherever they may be. Respect your users, and they pay you back. Kuznech Mobile Product Recognition technology offers exceptionally high potential for user engagement and retention, helping you increase your click through rates, customer engagement, and average basket size. Keep it simple: Mobile Product Recognition simplifies online purchasing for almost any product type to only three steps: Smartphone – Recognition – Purchase. Kuznech Mobile Product Recognition: we are here for you. To win. Vocabulary Digital fingerprint of an image Convolutional Neural Networks (CNN) A coded string of binary digits generated by a mathematical algorithm, uniquely identifies an image. Just like the analog fingerprint of a person, a fingerprint of an image cannot be reconstructed from any other digital fingerprint. CNN are useful to recognize visual patterns from pixel images with minimal preprocessing. They can recognize patterns with extreme variability and are robust to distortions. Hashing Hashing in general is a useful way to reduce a huge amount of data to a short(ish) number that can be used to identify that image. Hashing is used to index and retrieve images in a database because it is faster to find the item using the shorter hashed key than to find it using the original value. Vocabulary Deep learning Clustering Classification A field of computer science that seeks to mimic the human brain with hardware and software. Kuznech Visual Search and Recognition Engine is powered by convolutional neural networks, a type of deep-learning technology. Cluster analysis, or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). In machine learning and statistics, classification is the problem of identifying to which of a set of categories a new observation belongs. Kuznech Mobile Recognition: Nice to Meet You! Learn more about Kuznech Mobile Product Recognition: http://kuznech.com/products/mobile-recognition/ Feel free to contact us: http://kuznech.com/contacts/ [email protected] Interested in Mobile Recognition? We have a lot in common! Shall we talk it over? :)
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