title: GLOBAL PERFORMANCE PREDICTION FOR DIVERGENCE-BASED IMAGE REGISTRATION CRITERIA.
abstract: Divergence measures find application in many areas of statistics, signal processing and machine learning, thus necessitating the need for good estimators of divergence measures. While several estimators of divergence measures have been proposed in literature, the performance of these estimators is not known. We propose a simplekNN density estimation based plug-in estimator for estimation of divergence measures. Based on the properties ofkNN density estimates, we derive the bias, variance and mean square error xof the estimator in terms of the sample size, the dimension of the samples and the underlying probability distribution. Based on these results, we specify the optimal choice of tuning parameters for minimum mean square error. We also present results on convergence in distribution of the proposed estimator. These results will establish a basis for analyzing the performance of image registration methods that maximize divergence.
authors.affiliation: Department of EECS, University of Michigan, Ann Arbor, MI 48109.
authors.forename: Kumar
authors.initials: K
authors.lastname: Sricharan
authors.affiliation: School of EECS, Oregon State University, Corvallis, OR 97331.
authors.forename: Raviv
authors.initials: R
authors.lastname: Raich
authors.affiliation: Department of EECS, University of Michigan, Ann Arbor, MI 48109.
authors.forename: Alfred O
authors.initials: AO
authors.lastname: Hero
keywords: divergence estimation; kNN density estimators; performance characterization; plug-in estimators