Relative Karhunen-Loeve Transform Method for Pattern Recognition

Authors:
Affiliations:
Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

Citing 2
Cited 1

Application of the Karhunen-Loève Expansion to Feature Selection and Ordering

IEEE Transactions on Computers
Relative Karhunen-Loeve transform

IEEE Transactions on Signal Processing

ALSBIR: A local-structure-based image retrieval

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

CLAFIC (CLAss-Featuring Information Compression) is a well-known class feature extraction method. By using Karhunen-Lo猫ve transform (KLT) for patterns in a category, class features for the category are extracted. However, such a class feature may not be suitable for classification, if it is also contained in other categories. Suitable class features for classification have to be contained in a category but not in the other categories. In order to solve this problem, we propose the relative Karhunen-Lo猫ve transform method (RKLTM) for class feature extraction. We show the advantages of RKLTM over CLAFIC by the experiments on handwritten numeral recognition.