Objective
The aim of this project is to advance the state of the art of cognitive systems by developing a methodology for autonomous and continuous learning. The project will concentrate on structural learning, where relations between components and compositional hierarchies play a central role in object categorization. Such learning is particularly relevant for the interpretation of man-made objects, hence the project will use the recognition of buildings and parts of buildings in outdoor scenes as its exemplary application domain.
Due to the diversity of shapes and spatial arrangements of the different parts of a building, the recognition system must be capable of continually updating its conceptual knowledge. This requires the development of innovative methods for continuous learning. The project will advance the state of the art by concentrating on techniques of pattern discovery, concept learning, and ultimately self-learning. Just like a human child which has to be taught not only a certain subject but also the skills of autonomous learning, the proposed system will incorporate several levels of learning with decreasing responsibility of the teacher and increasing autonomy of the trained system, developing some self-awareness.
The project will use symbolic primitives extracted by low-level modules. The relationships between the extracted components will be represented by
- Bayesian networks, which will be used to model hierarchical structures,
- Markov Random Fields, which will be used to model peer-to-peer relations,
- logical structures which represent taxonomical and compositional hierarchies, and
- 2D grammars which will attempt to capture the structural relations syntactically.
For the development and evaluation of the system the project will use a rich dataset of several thousands of images of urban environments in the different countries of the participants. The learning components will be developed around the knowledge-based interpretation system SCENIC.
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.
You need to log in or register to use this function
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.
Data not available
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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
53113 BONN
Germany
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.