Structured Prediction Models in Computer Vision – and – Efficient Convex Relaxation of Mixture Regression with Application to Motion


June 10, 2009


Tiberio Caetano


Australian National University


TALK 1 – Structured Prediction Models in Computer Vision

Abstract: I’ll present a summary of our recent work on using modern machine learning methods to solve computer vision problems. This essentially consists of using structured prediction models like max-margin structured estimators and conditional random fields. The computer vision problems we will discuss include graph matching, shape classification and object categorization.

TALK 2 – Efficient Convex Relaxation of Mixture Regression with Application to Motion Segmentation

Abstract: We give a semidefinite relaxation for maximum a posteriori estimation of a mixture of regression models. In addition we show how the semidefinite program can be exactly solved by a fast spectral method. We compare the proposed technique against Expectation-Maximization for synthetic problems as well as for problems of motion segmentation in computer vision, with promising results.


Tiberio Caetano

Tiberio Caetano studied Electrical Engineering, Physics and Computer Science at the Universidade Federal do Rio Grande do Sul (UFRGS), Brazil, where he obtained the PhD degree with highest distinction in 2004. The research part of the PhD program was undertaken at the Computing Science Department at the University of Alberta, Canada. He held a postdoctoral research position at the Alberta Ingenuity Centre for Machine Learning and is currently a senior researcher with the Statistical Machine Learning Group at NICTA. He is also an adjunct research fellow at the Research School of Information Sciences and Engineering, Australian National University.His research interests include pattern recognition, machine learning and computer vision. More information at