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.
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: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2011-IEF
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
SW7 2AZ London
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.