To represent its content relevant features have to identified and extracted from it and the image model is the logical framework to organize those features. High level symbolic model needs to represent the knowledge about the image objects contained into the image and the relationships in between them. Features refer to image characteristic being used to define the visual contents of an image whereas semantics denote domain concepts manifested in the image. Whereas some automatic procedures are able to compute the most simple features, image processing is no even able to neglet the human involvement when extracting complex features. Also deriving semantics requires considerable human effort and this step is carried out by adopting a specific knowledge domain.
Structured data express image attributes through words.
Unstructured data representing image objects and their visual characteristics are obtained by image processing with semi-automated techniques. Both of the first two types give a description of image content but individually taken does not fully define the characteristics of the image. SQL manages data manipulation and information retrieval. In figure 1 the scheme for a typical conventional database is shown. Nevertheless some shortcomings of the standard DBMS limit their applicability to image retrieval.