Seminar Announcement
From Cliques to Equilibria: Dominant-Set Clustering and Its Applications
Dr. Marcello Pelillo of Ca'Foscari University of Venice
Friday, December 9, 2016 · 11:00AM · HEC 113
Abstract
The talk will provide an overview of "dominant sets," a graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs and has intriguing connections with optimization theory and (evolutionary) game theory. The idea is general and can be applied to weighted graphs, digraphs and hypergraphs alike. After introducing the basic properties of dominant sets, along with some generalizations, I’ll discuss a few recent computer vision applications, including interactive image segmentation and group behavior analysis.
Biography
Marcello Pelillo is Professor of Computer Science at Ca' Foscari University in Venice, Italy, where he directs the European Centre for Living Technology (ECLT) and the Computer Vision and Pattern Recognition group. He held visiting research positions at Yale University, McGill University, the University of Vienna, York University (UK), the University College London, and the National ICT Australia (NICTA). He has published more than 200 technical papers in refereed journals, handbooks, and conference proceedings in the areas of pattern recognition, computer vision and machine learning. He is General Chair for ICCV 2017, Track Chair for ICPR 2018, and has served as Program Chair for several conferences and workshops, many of which he initiated (e.g., EMMCVPR, SIMBAD, IWCV). He serves (has served) on the Editorial Boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition, IET Computer Vision, Frontiers in Computer Image Analysis, Brain Informatics, and serves on the Advisory Board of the International Journal of Machine Learning and Cybernetics. Prof. Pelillo has been elected a Fellow of the IEEE and a Fellow of the IAPR, and has recently been appointed IEEE SMC Distinguished Lecturer. His Erdos number is 2.
