MIESON
Multimedia Information Extraction from Social Networks
| Coordinatore | TELEFONICA INVESTIGACION Y DESARROLLO SA
Organization address
address: RONDA DE LA COMUNICACION S/N DISTRITO C EDIFICIO OESTE I contact info |
| Nazionalità Coordinatore | Spain [ES] |
| Totale costo | 163˙843 € |
| EC contributo | 163˙843 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2009-IOF |
| Funding Scheme | MC-IOF |
| Anno di inizio | 2011 |
| Periodo (anno-mese-giorno) | 2011-02-01 - 2013-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
TELEFONICA INVESTIGACION Y DESARROLLO SA
Organization address
address: RONDA DE LA COMUNICACION S/N DISTRITO C EDIFICIO OESTE I contact info |
ES (MADRID) | coordinator | 163˙843.34 |
Mappa
Word cloud
Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.
Obiettivo del progetto (Objective)
'Vast amounts of Multimedia data are being generated nowadays all around the world. A number of complementary factors have contributed to this explosion of content: affordability of high quality capturing gear, miniaturization and ubiquity of devices, and, especially, the Internet as an efficient technology to store and transmit the data. The Internet has deeply impacted the way we interact with information. It has provided the means for every user to air their own contents with extreme ease. The traditional information flow model, coming from a few sources to many consumers, has been replaced with this many-to-many paradigm. A plethora of problems related to search and discovery of resources is generated in this complex scenario: users have difficulties finding the content they care about, and demand facilities to discover new potentially appealing assets. While traditional Information Retrieval research has investigated solutions to search and discover relevant content in large-scale databases of text documents, multimedia information is still difficult to retrieve using automatic tools. Multimedia analysis methods find their main limitation in the so-called semantic gap, i.e. the difficulty of inferring high-level concepts from the set of low-level features which can be extracted from multimedia data. The objective of the MIESON project is to address the shortcomings of state of the art technology in multimedia data analysis and retrieval in order to provide effective tools for searching and discovering relevant content in real world applications. A multi-disciplinary approach will be used to bring together expertise from different areas in an effort to construct novel methodologies to fuse content and context information that would overcome current limitations. The MIESON project considers a user-centric approach; tools, methods and technology developed will focus on enhancing information retrieval and user experience in multimedia-enabled contexts.'
Introduzione (Teaser)
The amount of multimedia content has exploded, but searching this galaxy of footage to find relevant information is difficult since searches are textual rather than audio or visual. An EU-funded project has come up with a new search methodology to help resolve this challenge.
Descrizione progetto (Article)
Mind-boggling quantities of multimedia content are generated every day. For example, some 60 hours of video are uploaded every minute on YouTube alone.
However, the contemporary many-to-many paradigm of information flow presents a number of significant challenges, particularly when it comes to searching for and finding relevant information. This is especially tricky for multimedia content, since searches are mostly conducted using text, not images or sounds.
With EU backing, the 'Multimedia information extraction from social networks' (MIESON) project aimed to bolster the effectiveness of tools designed to access, discover or create multimedia content in real-world applications. The project focused on leveraging cutting-edge technology to create a novel user-centric approach to information retrieval.
Project members developed methods for learning to develop statistical models from the combination of content and context information. This has resulted in three new methods, including an approach that leverages unstructured contextual information from user-generated content.
To date, two scientific articles and a conference paper on the project have been published, and MIESON has attended several talks and various scientific research events.
Once incorporated into applications, the findings of the project should help make the searching of multimedia content that bit more intuitive.