Visual Fashion Finder, Mobile Image Search for Brands and Retailers Now Commercially Available from Cortexica

LONDON, April 9, 2013 /PRNewswire/ -- Cortexica Vision Systems, the leading provider of cloud-based image recognition systems and visual search technology, today announced the commercial availability of "Visual Fashion Finder," a mobile visual search solution for the fashion industry.

Visual Fashion Finder leverages Cortexica's patent-pending Find Similar™ technology, and can be integrated into the mobile applications of any brand or retailer. Visual Fashion Finder allows consumers to take a picture of an item of clothing or fashion accessory with a mobile device, and automatically finds similar items from a database of inventory.  

Instead of searching by text or drop down menus, Visual Fashion Finder empowers shoppers to search for clothes and accessories by uploading a photograph of what inspires them to their favorite brands' and retailers' Cortexica Find Similar™-enabled mobile apps. Within seconds of submitting their photos, users receive a comprehensive list of products that are similar to their submission in colour, pattern and texture.

Find Similar™ technology is part of Cortexica's award-winning, bio-inspired visual recognition platform. It mimics processes found in the visual cortex within the human brain to recognize content from digitally captured images and videos.

Cortexica's CEO Iain McCready, explained the challenges Cortexica overcame to be able to visually recognize fashion and recommend similar inventory. "When a user submits a photo using Visual Fashion Finder, we have to overcome a variety of technical challenges in a seamless way to make this an easy to use end-user experience.  These include poor lighting conditions and visual 'clutter,' reflections, background scenery, and other people or objects in the photo. No other system can provide this 'natural' recognition. Cortexica's Find Similar™ technology is designed to focus in on what users are looking for and 'see' images in the same way that the human eye sees them. Much like human visualization, our technology adapts superbly to challenging conditions, enabling the recognition of the image and, thus, returns an accurate match."

Cortexica's Visual Fashion Finder and Find Similar technology rely on NVIDIA GPUs to identify and match images. The technology was first demonstrated at NVIDIA's GPU Technology Conference (GTC) on March 19, 2013, during the keynote address by NVIDIA co-founder and CEO Jen-Hsun Huang, who commented, "This is really fantastic technology that takes visual search to the next level".  He went on to add, "This will take shopping for fashion to a whole new level."        

View Huang's remarks about Cortexica during his GTC keynote presentation here: www.youtube.com/watch?v=5uzvx1bAUFo&fmt=22&hd=1#t=11m41s.  

Visual Fashion Finder, Find Similar™, and Exact Match technologies are now commercially available as a hosted service for integration into fashion retailers' and brands' mobile and online applications.  For more information, please Email.

About Cortexica

Cortexica Visual Systems Ltd. is the leading provider of cloud-based image recognition systems and visual search technology. Our vision search platform is used by global retailers, brands, and mobile application developers to power real-time "Exact Match Image Search" and "Find Similar Image Search" within large databases of product images. Cortexica was founded in 2009, after six years of research in the Bioengineering labs of Imperial College London, where researchers effectively modeled how human neurons react to visual stimulus, accurately mimicking the Primary Visual Cortex. The Cortexica research team built a computerized cortical key-point based image recognition technology upon which modules for different applications of image recognition can be built for a product or service – eg: looking for a logo in a stream of video or matching an image against a database of images. For more information visit www.cortexica.com

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SOURCE Cortexica Visual Systems Ltd.