Due to the huge increase in the amount of digital images available in the ‘‘Internet era”, making efficient Content Based Image Retrieval (CBIR) systems has become one of the major endeavors. In this paper, the authors study the integration of an automatic generated knowledge base in a CBIR system based on relevance feedback method. An extensive analysis of the database structure has been carried out using fuzzy clustering algorithms to build the knowledge base. This knowledge base is used to make users aware of the overall organization of the image database during the query process. The relevance feedback method has been used to model the cluster structure as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session. The results presented in this paper demonstrate that this approach provides accurate retrieval results showing acceptable interaction speed that can be compared with existing methods.
|Titolo:||A knowledge based approach for a fast image retrieval system|
AMATO, ALBERTO (Primo)
|Parole Chiave:||Fuzzy clustering; Image retrieval; Knowledge; Multi-query relevance feedback|
|Data di pubblicazione:||1-nov-2008|
|Digital Object Identifier (DOI):||10.1016/j.imavis.2008.01.005|
|Appare nelle tipologie:||01 - Articolo su periodico|