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AISB event Bulletin Item

CFP: IEEE Online Learning for Classification Workshop, CVPR 2008

Call for Papers

IEEE Online Learning for Classification Workshop, CVPR 2008

There is a best paper award!

Online learning for classification is becoming essential for many real-world vision tasks. Amount of the available training data is increasingly rapidly, which makes the offline training times much longer, sometimes up to weeks. Online algorithms process one example at a time, thus, such methods are more adequate for large data sets. The performance of most offline classification methods is bounded with the amount of the priori information at the beginning since they assume the learner plays no role in obtaining information about the unknown classes. However, online algorithms can adapt to new information. In conventional methods, the training samples are simply drawn independently from some probability distribution. On the other hand, recent online works show more powerful oracles. Offline methods imply that the training and testing are separate steps. To our advantage, training the classifier online as new data arrives enables combining these stages. 

OLC workshop will bring together computer vision researchers interested in providing solid foundations to this promising and challenging area. The topics of interest include: 
  * Online methods for automatic object detection and tracking, 
  * Active learning for object identification and recognition, 
  * Incremental fusion of multi-modal data for classification tasks, 
  * Online and adaptive event detection, 
  * Applications using online classification methods, and 
  * Theoretical characterizations and various forms of performance bounds. 
In addition, we encourage work towards a solid framework for benchmarking OLC algorithms.

Submission of full paper:         March 25th, 2008 
Notification of acceptance:      April 15th, 2008 
Camera ready:                        April 25th, 2008 
Workshop Day:                      June 27th, 2008


- Fatih Porikli, MERL (
- Matthew Brand, MERL 

Program Committee: 

Ahmed Elgammal, Rutgers University 
Baback Moghaddam, JPL 
Bogdan Raducanu, Universitat Autonoma de Barcelona 
Carlo Regazzoni, Univ. Genova 
Chris Wren, MERL 
Daniel Gatica-Perez, IDIAP 
David Suter, Monash Univ. 
Enis Cetin, Bilkent University 
George Bebis, Univ. Nevada, Reno 
Guoliang Fan, Oklahoma State University 
Horst Bischof, TU-Gratz 
James Ferryman, Univ. of Reading 
James Rehg, Georgia Tech 
Larry Davis, Univ. Maryland 
Lior Wolf, Tel Aviv University 
Mubarak Shah, Univ. Central Florida 
Nicu Sebe, Univ. Amsterdam 
Peter Meer, Rutgers University 
Peter Tu, GE Global Research 
Philip Torr, Oxford Brookes Univ. 
Pietro Perona, Caltech 
Riad I. Hammoud, Delphi Electronics & Safety 
Qiang Ji, Rensselaer Polytechnic Institute 
Rama Chellappa, Univ. Maryland 
Shai Avidan, Adobe Systems 
Stan Sclaroff, Boston University 
Tsuhan Chen, Carnegie-Mellon