The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Small sample learning during multimedia retrieval using BiasMap
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.
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Cited by
Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.256 · Cited by 1,573
Metadata
- Authors
- Zhou, X. S., & Huang, T. S.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-24
- Volume
- 1
- Pages
- I-11-I-17
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2133604244
- Cited by
- 293
- References
- 14
- FWCI
- 32.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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