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The complex nature of two-dimensional image data has presented problems for traditional information systems designed strictly for alphanumeric data. Systems aimed at effectively managing image data have generally approached the problem from two different views: They either possess a strong database component with little image understanding, or they serve as an image repository for computer vision applications, with little emphasis on the image retrieval process. A general architecture for visual information-management systems (VIMS), which combine the strengths of both approaches, is presented. The system utilizes computer vision routines for both insertion and retrieval and allows easy query-by-example specifications. The vision routines are used to segment and evaluate objects based on domain-knowledge describing the objects and their attributes. The vision system can then assign feature values to be used for similarity-measures and image retrieval. A VIMS developed for face-image retrieval is presented to demonstrate these ideas.>
Bach et al. (Fri,) studied this question.