Index-based object recognition in pictorial data management

Abstract

We have been involved with formulating query processing strategies for an object-oriented, integrated, textual/iconic database management system. For this task, model-based representations of images by content are being used, as well as a new, efficient object recognition approach which also permits the efficients insertion and deletion of object models, called data-driven indexed hypotheses. This paper emphasizes the data management aspects of this approach: the insertion and deletion of object models, a performance/space trade-off which can be used to improve the recognition capabilities of our approach, and a secondary memory implementation of our approach