Leonidas J. Guibas : 2010 Plenary Session


Wednesday, April 28, 2010
Location: Fisher Conference Center, Arrillaga Alumni Center

"Image Webs: Computing and Exploiting Connectivity in Image Collections"


The widespread availability of digital cameras and ubiquitous Internet access have facilitated the creation of massive image collections. These collections can be highly interconnected through implicit links between image pairs viewing the same or similar objects. We propose building graphs called Image Webs to represent such connections. While earlier efforts studied local neighborhoods of such graphs, we are interested in understanding global structure and exploiting connectivity at larger scales. We show how to efficiently construct Image Webs that capture the connectivity in an image collection using spectral graph theory. Our technique can link together tens of thousands of images in a few minutes using a computer cluster. We also demonstrate applications for exploring collections based on global topological analysis.


Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, Stanford, MIT, and DEC/SRC. He has been at Stanford since 1984 and is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering). He heads the Geometric Computation group and is part of the Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas' interests span computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms -- all areas in which he has published and lectured extensively. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover's distance, Kinetic Data Structures (KDS), and Metropolis light transport. Professor Guibas is an ACM Fellow and a winner of the ACM Allen Newell award.