2010 Meetings

December 15, 2010: "From Clusters to Perceptions: The Evolution of Fuzzy Clustering" by Dr. Enrique H. Ruspini

Abstract

The ever-increasing amount of data accessible through computer networks has spurred the application of methods for its analysis, summarization, and interpretation. Clustering techniques, in particular, have been employed to discover knowledge and to reduce data complexity. This body of techniques, often described as performing “numerical classification” or “unsupervised pattern recognition,” encompass a wide diversity of procedures having different computational goals, which are rarely made either explicit or clear by their developers. Since their inception, clustering approaches based on the theory of fuzzy sets have been extensively applied due to their rich representational capabilities, their formal mathematical underpinnings, and the relations between the nature of fuzzy classifications and utilitarian and metric concepts such as preferences and similarities. Furthermore, the very nature of fuzzy clustering methods readily permits the definition of interesting data structures as instances of paradigmatic models that are approximated by subsets of the dataset being analyzed.

In our presentation, we will review the motivation and evolution of fuzzy-set based methods to discover structures in data. Our point of departure will be the initial proposal for the formulation of relational fuzzy clustering as an optimization problem over the set of all partitions of a subset of a metric space. We will also examine the related problem of partitioning a subset of a vector space and discuss major approaches to its treatment. Continuing our retrospective examination of fuzzy-clustering techniques we will focus on significant milestones in the evolution of this methodology including the generalizations of the notions of prototype, clustering, and fuzzy partition. The objective of this review is to motivate the introduction of qualitative object description methods, which are soft-computing based approaches for the representation of complex objects in terms of significant qualitative features (e.g., interesting substructures in biological molecules) and by identification of qualitative relationships between those features (e.g., spatial relations between features). We will conclude with a brief discussion of recent applications of QOD methods to important problems in the applied sciences.

Biography

Dr. Enrique H. Ruspini
Dr. Enrique H. Ruspini is Principal Researcher and Director of the Collaborative Soft Intelligent Systems Laboratory at the European Centre for Soft Computing in Mieres (Asturias), Spain. Dr. Ruspini received his degree of Licenciado en Ciencias Matemáticas from the University of Buenos Aires, Argentina, and his doctoral degree in System Science from the University of California at Los Angeles. Prior to joining ECSC, he was a Principal Scientist with the Artificial Intelligence Center of SRI International (formerly Stanford Research Institute). Dr. Ruspini has also held positions at the University of Buenos Aires, the University of Southern California, UCLA's Brain Research Institute, and Hewlett-Packard Laboratories. Dr. Ruspini is one the earliest contributors to the development of fuzzy-set theory and its applications, having introduced its use to the treatment of numerical classification and clustering problems. He has also made significant contributions to the understanding of the foundations of fuzzy logic and approximate-reasoning methods. His recent research has focused on the application of fuzzy-logic techniques to the development of systems for intelligent control of teams of autonomous robots, distributed intelligent control, intelligent data analysis, information retrieval, qualitative description of complex objects, and knowledge discovery and pattern matching in large databases.

Dr. Ruspini, who has lectured extensively in the United States and abroad and is the author of over 100 original research papers, is a Life Fellow of the Institute of Electrical and Electronics Engineers, a First Fellow of the International Fuzzy Systems Association, a Fulbright Scholar, an European Union Marie Curie Fellow, and a SRI Institute Fellow. Dr. Ruspini was the General Chairman of the Second IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'93) and of the 1993 IEEE International Conference on Neural Networks (ICNN'93). In 2004, Dr. Ruspini received the Meritorious Service Award of the IEEE Neural Networks Society for leading the transition of the Neural Networks Council into Society status. He is one of the founding members of the North American Fuzzy Information Processing Society and the recipient of that society's King-Sun Fu Award. Dr. Ruspini is the recipient of the 2009 Fuzzy Systems Pioneer Award of the IEEE Computational Intelligence Society. Dr. Ruspini is a former member of the IEEE Board of Directors (Division X Director , 2003–2004), the Past-President (President-2001) of the IEEE Neural Networks Council (now IEEE Computational Intelligence Society) and its past Vice-president of Conferences. Dr. Ruspini, who has led numerous IEEE technical, educational, and organizational activities, is also a member of the Administrative Committee of the IEEE Computational Intelligence Society, and of its Strategic Planning (Chair), Nominations and Appointments and Constitution and Bylaws Committees. Dr. Ruspini is also the Chair of the 2012 Frank Rosenblatt Technical Field Award Committee.


June 10, 2010: "Mobile Visual Search" by Prof. Bernd Girod, Stanford University

Abstract

Handheld mobile devices, such as camera phones or PDAs, are expected to become ubiquitous platforms for visual search and mobile augmented reality applications. A visual database for mobile image matching is typically stored in the cloud. Hence, for a visual comparison, information must be either uploaded from the mobile to the server, or downloaded from the server to the mobile. With relatively slow wireless links, the response time of the system critically depends on how much information must be transferred in both directions. We review recent advances in mobile matching, using a "bag-of-visual-words" approach with robust feature descriptors, and show that dramatic speed- ups are possible by considering recognition and compression jointly. We will use real-time implementations for different example applications, such as recognition of landmarks or media covers, to show the benefit from image processing on the phone, the server, and/ or both.

Biography

Bernd Girod is Professor of Electrical Engineering and (by courtesy) Computer Science in the Information Systems Laboratory of Stanford University, California, since 1999. Previously, he was Professor of Telecommunications in the Electrical Engineering Department of the University of Erlangen-Nuremberg. His current research interests are in the areas of video compression and networked media systems. He has published over 400 conference and journal papers, as well as 5 books, receiving the EURASIP Signal Processing Best Paper Award in 2002, the IEEE Multimedia Communication Best Paper Award in 2007, the EURASIP Image Communication Best Paper Award in 2008, as well as the the EURASIP Technical Achievement Award in 2004. As an entrepreneur, Professor Girod has been involved with several startup ventures as founder, director, investor, or advisor, among them Polycom (Nasdaq:PLCM), Vivo Software, 8x8 (Nasdaq: EGHT), and RealNetworks (Nasdaq: RNWK). He received an Engineering Doctorate from University of Hannover, Germany, and an M.S. Degree from Georgia Institute of Technology. Prof. Girod is a Fellow of the IEEE, a EURASIP Fellow, and a member of the German National Academy of Sciences (Leopoldina).