Model-driven development of content-based image retrieval systems

Generic systems for content-based image retrieval (CBIR), such as QBIC [7] cannot be used to solve domain-specific image retrieval problems, as for example, the identification of manuscript writers based on the visual characteristics of their handwriting. Domain-specific CBIR systems currently have to be implemented bottom up, i.e. almost from scratch, each time a new domain-specific solution is sought. Inspired by the recognition, that CBIR systems, although developed for different domain-problems, comprise similar-building blocks and architecture, the idea of adopting model-driven development techniques for generating CBIR systems was elaborated. To support the design of domain-specific CBIR-Systems on a conceptual level by reusing data structure and functional interfaces a framework model is developed, which can be used to derive concrete domain-specific CBIR models. A transformation approach for the generation of a platform-specific implementation on top of an object-relational database from the concrete conceptual model is proposed. Finally, how these techniques can be applied for the design of a CBIR system for the identification of music manuscript writers based on the visual characteristics of their handwriting is demonstrated.