Talk: Consensus Learning

03:00 - 3:45 PM (IST), December 05, 2020, 5th IAPR International Conference on Computer Vision & Image Processing

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.

Speaker's Biography

Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from University of Bern, Switzerland. He was an associate professor at Technical University of Berlin, Germany. Since 2002 he is a full professor at University of Münster, Germany, and currently the Dean of the Faculty of Mathematics and Computer Science. He is Editor-in-Chief of International Journal of Pattern Recognition and Artificial Intelligence. In addition, he also serves on the advisory board and editorial board of several other journals including IEEE Transactions on Medical Imaging and International Journal of Neural Systems. His research interests include image analysis, pattern recognition, and machine learning. Prof. Jiang is a Fellow of IAPR and senior member of IEEE.