Divergence Matching Criteria for Registration, Indexing and Retrieval

Wednesday, January 31, 2001 - 2:00pm - 3:00pm
Lind 400
Alfred Hero III (University of Michigan)
We review, motivate and apply the Renyi divergence measure for classification and detection tasks arising in database matching. The Renyi divergence is a generalization of the Kullback-liebler divergence and the Hellinger distance for measuring differences between multivariate probability densities. This divergence measure is motivated directly from Chernoff's theorem on the asymptotic probability of error rate of the optimal discrimanant. To be applied to image registration, indexing, or retrieval the Renyi divergence must be estimated from the data. There are two ways to accomplish this: 1) non-parametric density estimation; 2) minimal graph matching via minimal spanning trees or other quasi-additive optimal graph structure. We will present both theoretical theoretical results and implementations of Renyi matching criteria for a range of problems relevant to searching databases.