A similarity measure based on causal neighbours and mutual information
Design and application of hybrid intelligent systems
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The need for systems that can stock, represent, and provide efficient retrieval facilities ofimages of particular interest is becoming very high in medicine. In this respect, a lot of workhas been done to integrate image data in the standard data processing environments. The two different approaches used for the representation of images are: the metadata-based and the content-based approaches. Users in medicine need queries that use both content-based and metadata representation of images or salient objects. In this paper, we first present a global image data model that supports both metadata and low-level descriptions of images and their salient objects. This allows us to make multi-criteria image retrieval (context-, semantic-, and content-based queries). Then, we present an image data repository model that captures all the data described in the model and permit to integrate heterogeneous operations in a DBMS. In particular, content-based operations (content-based join and selection) in combination with traditional ones can be carried out using our model.