Rule Based Strategies for Image Interpretation

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 3

Vision and Robotics

We present an interpretation system which utilizes world knowledge in the form of simple object hypothesis rules, and more complex interpretation strategies attached to object and scene schemata, to reduce the ambiguities in image measurements. These rules involve sets of partially redundant features each of which defines an area of feature space which represents a "vote" for an object. Convergent evidence from multiple interpretation strategies is organized by top-down control mechanisms in the context of a partial interpretation. One such strategy extends a kernel interpretation derived through the selection of object exemplars, which represent the most reliable image specific hypotheses of a general object class, resulting in the extension of partial interpretations from islands of reliability.