A pictorial index mechanism for model-based matching

Abstract We are currently developing unified query processing strategies for image databases. To perform this task, model-based representations of images by content are being used, as well as a hierarchical generalization of a relatively new object-recognition technique called data-driven indexed hypotheses . As the name implies, it is index-based, from which its efficiency derives. Earlier approaches to data-driven model-based object recognition techniques were not capable of handling complex image data containing overlapping, partially visible, and touching objects due to the limitations of the features used for building models. Recently, a few data-driven techniques capable of handling complex image data have been proposed. In these techniques, as in traditional databases, iconic index structures are employed to store the image and shape representation in such a way that searching for a given shape or image feature can be conducted efficiently. Some of these techniques handle the insertion and deletion of shapes and/or image representations very efficiently and with very little influence on the overall system performance. However, the main disadvantage of all previous data-driven implementations is that they are main memory based. In the present paper, we describe a secondary memory implementation of data-driven indexed hypotheses along with some performance studies we have conducted.