Online Learning for Classification Workshop, CVPR 2007

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. There will be an “OLC Challenge” with a benchmark classification data as an extension of this workshop in 2008. 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.