ORGANIZING LARGE STRUCTURAL MODELBASES

We present a hierarchically structured approach to organizing large structural modelbases using an information theoretic criterion. Objects (patterns) are modeled in the form of random parametric structural descriptions (RPSDs), an extension of the parametric structural description graph-theoretic formalism [1]. Objects in scenes are modeled as parametric structural descriptions (PSDs). The organization process is driven by pairwise dissimilarity values between RPSDs. We also introduce the node pointer lists, which are computed offline during modelbase organization. During recognition, the only exponential matching process involved is between the scene PSD and the RPSD at the root of the organized tree. Using the organized hierarchy along with the node pointer lists, the remaining work simplifies to a series of inexpensive linear tests at the subsequent levels of the tree search. We develop the theory and present three modelbases: 50 objects built from real image data, 100 CAD models, and 1000 synthetic abstract models.