Statistical learning for effective visual information retrieval

3 June 2004

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 3, 609
https://doi.org/10.1109/icip.2003.1247318

Abstract

For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: scarcity of training data, and imbalance of training classes. We present these challenges and outline our methods for addressing them: active learning, recursive subspace co-training, adaptive dimensionality reduction, class-boundary alignment, and quasi-bagging.

Keywords

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