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Cited by 1,620

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Using discriminant eigenfeatures for image retrieval

Swets, D.L., Weng, J.J.·1996·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as "well-framed" views, and compare it with that of the principal component analysis.

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Metadata

Authors
Swets, D. L., & Weng, J. J.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
1996
Volume
18
Issue
8
Pages
831-836
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2012352340
Cited by
1,620
References
1
FWCI
33.28
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Using discriminant eigenfeatures for image retrieval. https://app.cochat.ai/papers/10.1109/34.531802
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Swets, D. L., & Weng, J. J. (1996). Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.531802

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.