Using discriminant eigenfeatures for image retrieval
- 1 January 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 18 (8), 831-836
- https://doi.org/10.1109/34.531802
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.Keywords
This publication has 16 references indexed in Scilit:
- Genetic algorithms for object recognition in a complex scenePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Nonlinear manifold learning for visual speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Efficient image retrieval using a network with complex neuronsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Metadata in video databasesACM SIGMOD Record, 1994
- View-based and modular eigenspaces for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- OVID: design and implementation of a video-object database systemIEEE Transactions on Knowledge and Data Engineering, 1993
- A visual information management system for the interactive retrieval of facesIEEE Transactions on Knowledge and Data Engineering, 1993
- Automatic generation of object recognition programsProceedings of the IEEE, 1988
- Nearest neighbor pattern classificationIEEE Transactions on Information Theory, 1967
- THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTSAnnals of Eugenics, 1938