CMU VASC Seminar: Metric Learning for Image Alignment and Classification

VASC Seminar
Monday, September 8, 2008

Metric Learning for Image Alignment and Classification
Minh Hoai Nguyen
Robotics Institute, Carnegie Mellon University

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.


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.

Swem: add this to your reading list. -Bob