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MICO - Media in context

Research project MICO aims to provide cross-media analysis solutions for online multimedia producers.

MICO is a research project partially funded by the European Commission 7th Framework Programme. MICO is set up as an interdisciplinary effort involving leading European experts in the fields of semantic-web and linked-data technologies, distributed high-dimensional databases, natural language processing and multimedia extraction.

Project overview

Project period:

2013-05-01 2016-04-30


MICO (Media in Context) is a research project partially funded by the European Commission 7th Framework Programme (grant agreement no: 610480).

Participating departments and units at Umeå University

Department of Computing Science

Project description

The consortium consists of 7 participants with complementary expertise, of which two are industrial research organizations with a strong application focus (Salzburg Research and Fraunhofer), three are universities providing a solid formal backing for the project (University of Passau, UMEA University and University of Oxford), of which one is running the most successful crowd sourcing platform for academic research, two are innovative SMEs in the content management and media domains (InsideOut10 and Zaizi Ltd).

With the rapid growth of multimedia content on the Web and in corporate intranets, discovering hidden semantics in raw multimedia is becoming one of the biggest challenges and a significant business opportunity for online multimedia producers and users. Multimedia analytics industry is still in its infancy and due to the complexity and the expensive price tags of the existing multimedia analytics products, SMEs and multi-media producers are unable to reap the benefits of modern cross media analysis technologies.

MICO project aims to solve these problems by providing an integrated platform for cross media analysis, metadata publishing, querying and recommendation. MICO develops models, standards and software tools to jointly analyse, query and retrieve hidden semantics from connected and related media objects (text, image, audio, video, office documents) to provide better information extraction results for more relevant search and information discovery in multimedia content.