Feature Extraction and Similarity Search in Large Databases (EMPA) |
Extraction, analysis and preprocessing of feature vectors as well as nearest neighbor search in (high-dimensional) feature spaces are the key components in a system that supports sophisticated image retrieval from a large collection of images. These areas traditionally belong to different fields. Image processing and graphics have considered feature extraction mainly for the purpose of compression and transmission while database research has considered indexing schemes for attribute-based retrieval in relatively low dimensional spaces. In this proposal for a joint research project, two groups from these two fields (the Database Group around Hans-J. Schek and the Computer Graphics Group around M. Gross) work together in order to solve the underlying fundamental problems: the evaluation and improvement of existing low-level signal analysis methods (so-called feature extraction methods); the influence of the dimensionality of features for the indexing and retrieval cost and for the quality of the result; the development of suitable query languages and querying metaphors that are intuitive for the user; and the adaptation of the so-called relevance feedback technique that is commonly used in information retrieval to enhance the result of a former query. |
Term |
24 month |
Funding |
SFr. 140.000.- by Dr. K. Simon (EMPA) |
Partners |
Prof. M. Gross |
Prof. B. Schiele |
Dr. K. Simon |
Related Research Area |
Multimedia Information Management |
contacts: Prof. Dr. H.-J. Schek |