Objective
Europe’s media industry lacks an effective business model for their online content, particularly in the context of falling revenues from traditional online advertising – they need new ways to effectively monetise their content.
Our aim is to allow media to generate revenue from their online images.
We apply innovative image-analysis technology to detect clothes in online images, and match them with clothes in retailers’ online product catalogues. This creates a lucrative collaboration between online media and retailers – for every customer redirected to a retailers’ website via an online image, media publications receive a commission fee.
An initial version of this automated technology (running in under 300 milliseconds) has already been developed. We can recognise photos of clothes in isolation (an image of an item of clothing on a plain background) and match them to the clothes sold by our 8000 partner retailers (Amazon, H&M…). We then give readers the opportunity to click to find a link to buy the same or similar item. This solution has already been commercialised to several leading magazines (including Closer, Grazia & Be), who have seen online advertising revenues increase by up to 6 times.
However, photos of clothes in isolation represent only a small proportion of online images.
Our ambition is therefore to adapt our technology to detect clothes in photos of people – representing a much larger proportion of online images. Online readers will be able to click to “Get the Look” of a celebrity or public figure in an online photo, and will be shown links to retailers selling similar items. The aim of our feasibility study is to refine the technological specifications and validate the commercial proof of concept with a base of current and potential clients.
With ShopStar, we are creating a non-invasive advertising solution that can be deployed to international media groups worldwide, positioning Shopedia as category leader for this disruptive new approach
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencesdatabases
- engineering and technologymaterials engineeringtextiles
- social scienceseconomics and businessbusiness and managementbusiness models
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback.
You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Programme(s)
Call for proposal
(opens in new window) H2020-SMEInst-2014-2015
See other projects for this callSub call
H2020-SMEINST-1-2015
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
75004 PARIS
France
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.