Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.

The thesaurus is widely used in information indexing and retrieval for vocabulary control and concept search. Conceptual relationships are usually reciprocal and many-to-many, and can hardly be described precisely. To comply with the imprecise nature of the thesaurus, we have proposed and developed a fuzzy multilinkage thesaurus builder. The builder maintains a fuzzy membership degree for each relationship. The relationships are extensible and have an inheritance capability. The applications of the fuzzy multilinkage thesaurus builder to expand the query terms for multimedia information retrieval are presented.

47-55