Abstract
The tutorial will address the problem of recognizing visual object classes in images, currently the focus of
much interest in Computer Vision. As recent innovations in the area draw heavily on machine learning concepts, the
tutorial will attempt to highlight the growing intersection between the two areas. The material will be divided five
sections, covering (i) bag of words models; (ii) parts and structure models; (iii) discriminative methods; (iv)
objects and scenes (v) retrieval schemes for large datasets. The emphasis will be on the
important general concepts rather than in depth coverage of contemporary papers. The tutorial is a revised version of
the prize-winning short course given at ICCV 2005 and CVPR 2007 in conjunction with Fei-Fei Li (Princeton) and Antonio
Torralba (MIT) (Link).
Outline of content
Suitable audience
The tutorial is suitable for anyone interested in Object Recognition
as a problem in of itself, or as a target application for machine learning
tools. The material is suitable for 1st or 2nd year
graduate students and beyond.
Course Materials
Final version of course slides:
1. Introduction
2. Bag of Words models
3. Bag
of Words models with spatial information
4. Parts and Structure
5. Classifier-based methods
6. Combined segmentation and recognition
7. Recognition for retrieval
8. Datasets and Conclusions
Link
to previous incarnation of tutorial, given at CVPR 2007 and ICCV
2005.
Presenter's Background
Rob Fergus is currently an assistant professor of computer science at
the Courant Institute at New York University, USA. He received an MSc
from Caltech (with Prof. Pietro Perona), a PhD from Oxford (with
Prof. Andrew Zisserman). Before coming to NYU, he spent two years as a
post-doc in the Computer Science and Artificial Intelligence Lab
(CSAIL) at MIT, working with Prof. William Freeman. He won the prize
for the best paper at CVPR 2003 and the award for best computer
science thesis in the UK in 2005.