Small sample learning during multimedia retrieval using BiasMap
- 24 August 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (10636919)
- https://doi.org/10.1109/cvpr.2001.990450
Abstract
All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by the user is usually small; furthermore, they are not representative for the true distributions-especially the negative examples. Adding to the difficulties is the nonlinearity in real-world distributions. Existing solutions fail to address these problems in a principled way. This paper proposes biased discriminant analysis and transforms specifically designed to address the asymmetry between the positive and negative examples, and to trade off generalization for robustness under a small training sample. The kernel version, namely "BiasMap ", is derived to facilitate nonlinear biased discrimination. Extensive experiments are carried out for performance evaluation as compared to the state-of-the-art methods.Keywords
This publication has 8 references indexed in Scilit:
- Transform features for texture classification and discrimination in large image databasesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Exploring the nature and variants of relevance feedbackPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Boosting image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Optimizing learning in image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Edge-based structural features for content-based image retrievalPattern Recognition Letters, 2001
- Generalized Discriminant Analysis Using a Kernel ApproachNeural Computation, 2000
- The Nature of Statistical Learning TheoryPublished by Springer Nature ,1995
- Regularized Discriminant AnalysisJournal of the American Statistical Association, 1989