Skip to main content
European Commission logo
English English
CORDIS - EU research results
CORDIS
CORDIS Web 30th anniversary CORDIS Web 30th anniversary
Content archived on 2024-06-18

Emotionally-based Tagging of Multimedia Content

Objective

As the amount of digital multimedia content increases while the cost of storing digital multimedia decreases, it becomes very difficult to locate a specific content. To benefit from this wealth of multimedia data, numerous methods for automatic multimedia indexing and retrieval have been proposed. However, these methods usually depend on having large amounts of annotated data to learn from. The main idea behind Implicit Human-Centred Tagging (IHCT), which is the main topic of this proposal, is automatic extraction of tags based on user's behaviour or response while watching multimedia content. The underlying assumption of IHCT is that observable nonverbal behaviour while interacting with multimedia content (e.g. affective facial expressions, head nods and shakes, laughter, pupil dilation, etc.) provide information useful for improving the tags associated with the data. As such responses are generated naturally and spontaneously, no specific effort is required from the users, and this is why the resulting tagging is said to be “implicit” (rather than explicit). The main aim of the EmoTag project is to develop and evaluate an affect-sensitive implicit tagging system based on continuous assessment of affective responses of users. Specifically, the project aims to answer whether users' behaviour can be used to inform the system about the possible tags that could be used to describe the multimedia content and how automatic tools for multimedia content tagging could be redesigned to benefit from the implicit tags. The project will also investigates whether retrieval/recommendation systems based on implicit tags will perform comparably to those based on explicit tags. The other aspect of the proposed study deals with automatic continuous dimensional affect recognition from multiple modalities and proposes a number of novel machine learning techniques that promise to
solve this complex problem characterized by high-dimensional feature space and low amount of sample data.

Call for proposal

FP7-PEOPLE-2011-IEF
See other projects for this call

Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
EU contribution
€ 200 371,80
Address
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ LONDON
United Kingdom

See on map

Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
Administrative Contact
Shaun Power (Mr.)
Links
Total cost
No data