Probability, Pattern Recognition and Optimization Techniques for Interactive Media

par/by Christopher Pal
Ecole Polytechnique

In this talk I show how probability models and optimization techniques can be used to create core technologies for: computer vision, computational photography, natural language processing and data mining which enable new forms of interactive media.

Iıll begin by briefly examining some key tasks required to create rich, high resolution representations of visual scenes by combining images to: create high quality panoramic representations and infer three dimensional structure in a scene. I'll motivate and show how such representations can then be used as interactive interfaces to information.

Examining the tasks involved with associating semantic information in text with rich visual representations, I'll show how probability models based on random fields can be used to classify documents and extract semantic information. I'll highlight some of our recent work on constructing novel model structures and novel approaches for optimizing random field based models. In particular, Iıll discuss hybrid generative and discriminative techniques, applications to semi-supervised learning, Boltzmann machine based architectures and learning with contrastive divergence based methods.

Biography :

Christopher Pal is an Assistant Professor of Computer and Software Engineering at the École Polytechnique of Montreal. His research interests include computer vision, pattern recognition and machine learning with applications to computer graphics, natural language analysis and data mining.

He was previously an assistant professor of Computer Science with the University of Rochester. He has also held positions with the University of Massachusetts, the University of Toronto, Interval Research and Microsoft Researchıs Interactive Visual Media Group. He earned his doctorate from the University of Waterloo in Canada.