Xiaoyi Jiang has been a Full Professor at the University of Münster, Germany, where he is currently the Dean of the Faculty of Mathematics and Computer Science. He is a fellow of IAPR. He is the Chair of the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP). He is the Editor-in-Chief of International Journal of Pattern Recognition and Artificial Intelligence. He also serves on the Advisory Board and Editorial Board of several journals, including International Journal of Neural Systems and Journal of Big Data. Previously, he has been an Associate Editor of IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B/IEEE TRANSACTIONS ON CYBERNETICS, IEEE TRANSACTIONS ON MEDICAL IMAGING, and Pattern Recognition.
Combining multiple models into a consensus model helps, amongst others, to reduce the uncertainty in the initial models. Consensus learning can be formulated informally or formally in arbitrary problem domain. This talk focuses on the formal framework of the so-called generalized median computation. The concept of this framework and the related computation algorithms will be presented. A variety of applications in image analysis and pattern recognition will be shown to demonstrate the power of consensus learning.