QUEMAT: QUEry-adaptive Media Asset Tracking

FOCAL International (FOCAL) is the leading global trade association facilitating use of commercial footage and other content in all forms of media production, with over 300 international members comprising content libraries, archives, production researchers and service providers. The company has offered a popular "Footage Finder" service for many years, generating sales leads and routing these to members. The QUEMAT project creates novel deep learning technology (via technology partners Dithen and The Media Institute) to innovate a high-impact new "Footage Finder" service, leveraging the established footprint of FOCAL International, and in turn generating an abundance of learning and training data through adoption to continuously improve the project's deep learning performance. The QUEMAT sustainable ecosystem delivers 'network effects' inside and outside the project, yielding high-precision and commercially relevant results to visual search queries within Footage Finder and across an abundance of applications.

The project is ideally timed as inflection points have been reached concurrently in three disparate arenas: a) film and television production is burgeoning with new routes to market (e.g. Netflix now invest more in original production than CBS); b) deep neural network (DNN) technology has reached a level of maturity allowing investments in machine learning to deliver unprecedented returns; and c) technology to perform 'video signature extraction' (i.e., allowing for rapid search of video without the need to process pixels) has been validated by new MPEG standards activity: 'Compact Descriptors for Video Analysis' (CDVA), creating a foundation for widespread adoption.

The project will innovate the novel DNN (Deep Neural Network) -based media asset processing and discovery capabilities with the following unique features: i) it will allow for the continuous production of compact signatures per query type while remaining standards-compliant; ii) it will be specifically trained to be repurposed for various query types with an automated query-driven customization stage that can be performed offline; and iii) the operational complexity will be tunable and the entire software pipeline will be portable to any public cloud provider for easy scaling and adoption across media enterprises.

Overall, QUEMAT technology will deliver increased revenues to content owners and increase operational efficiencies at a time when content producers and footage libraries are struggling to reconfigure traditional value chains to benefit from new monetization opportunities. The project's three partners: FOCAL, The Media Institute and Dithen -- bringing together media industry presence and leading technology expertise in deep learning and media processing, and a record of successful collaboration.