Chapter

Energy Minimization Methods in Computer Vision and Pattern Recognition

Volume 2683 of the series Lecture Notes in Computer Science pp 147-163

Image Registration and Segmentation by Maximizing the Jensen-Rényi Divergence

  • A. Ben HamzaAffiliated withDepartment of Electrical and Computer Engineering, North Carolina State University
  • , Hamid KrimAffiliated withDepartment of Electrical and Computer Engineering, North Carolina State University

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Abstract

Information theoretic measures provide quantitative entropic divergences between two probability distributions or data sets. In this paper, we analyze the theoretical properties of the Jensen-Rényi divergence which is defined between any arbitrary number of probability distributions. Using the theory of majorization, we derive its maximum value, and also some performance upper bounds in terms of the Bayes risk and the asymptotic error of the nearest neighbor classifier. To gain further insight into the robustness and the application of the Jensen-Rényi divergence measure in imaging, we provide substantial numerical experiments to show the power of this entopic measure in image registration and segmentation.