Efficient Content-Based and Metadata Retrieval in Image Database

Managing image data in a database system using metadata has been practiced since the last two decades. However, describing an image fully and adequately with metadata is practically not possible. The other alternative is describing image content by its low-level features such as color, texture, shape, etc. and using the same for similarity-based image retrieval. However, practice has shown that using only the low-level features can not as well be complete. Hence, systems need to integrate both low-level and metadata descriptions for an efficient image data management. However, due to lack of adequate image data model, absence of a formal algebra for content-based image operations, and lack of precision of the existing image processing and retrieval techniques, no much work is done to integrate the use of low-level and metadata description and retrieval methods. 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 to make multi-criteria image retrieval (context-, semantic-, and content-based queries). Furthermore, we present an image data repository model that captures all data described in the model and permits to integrate heterogeneous operations in a DBMS. In particular, similarity-based operations (similarity-based join and selection) in combination with traditional ones can be carried out. Finally, we present an image DBMS architecture that we use to develop a prototype in order to support both content-based and metadata retrieval.