Event Location: NSH 1507
Bio: Minh Hoai Nguyen received his B.E. in Software Engineering from
University of New South Wales, Australia in 2005. He has been a Ph.D.
student in Carnegie Mellon University’s Robotics Institute since 2006
and is advised by Fernando de la Torre. His research interests are in
the area of computer vision and machine learning, especially at the
intersection of the two. He is particularly interested in using
data-driven techniques to learn representations of images (e.g. pixel
selection, non-linear pixel combination) that are optimal for
classification, clustering, visual tracking, and modeling.

Abstract: What constitutes good metrics to encode and compare? This talk will
address this fundamental question that concerns computer vision
scientists. We will show how to learn metrics that are optimal for image
alignment with Active Appearance Models (AAMs), and image classification
using Support Vector Machines (SVMs). Traditionally, feature
extraction/selection and metric learning methods have been inferred
independently of model estimation (e.g. SVM, AAM). Independently
learning features and model parameters may result in the loss of
information that is relevant to the alignment or classification process.
Rather, we propose a convex framework for jointly learning image metrics
and model parameters. To illustrate the benefits of our approach, this
talk is divided in two parts. In the first part, we will discuss the
problem of learning image metrics to avoid local minima in template
alignment and AAMs. We learn a cost function that explicitly optimizes
the occurrence of local minima at and only at the places corresponding
to the correct alignment parameters. In the second part of the talk, we
will consider the problem of building a fast classifier for facial
feature detection. We will show how to jointly learn SVM parameters
together with a subset of the pixels that are relevant for
classification. This work is done in collaboration with Joan Perez and
Fernando De la Torre.