Title :
A PCA based method for improving the reliability of bank note classifier machines
Author :
Ahmadi, Ali ; Omatu, Sigeru ; Kosaka, Toshihisa
Author_Institution :
Osaka Prefecture Univ., Sakai, Japan
Abstract :
This paper addresses the reliability of neuro-classifiers for bank note recognition. A local principal component analysis (PCA) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map (SOM) clustering and then the PCA is performed in each region. A learning vector quantization (LVQ) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. The experimental results taken from 1,200 samples of US dollar bills show that the reliability is increased up to 100% when the number of regions as well as number of codebook vectors in the LVQ classifier is taken properly.
Keywords :
bank data processing; document image processing; image classification; image coding; principal component analysis; reliability; self-organising feature maps; vector quantisation; PCA based method; bank note classifier machines; codebook vectors; learning vector quantization network; neuro-classifiers; principal component analysis method; self-organizing map clustering; Cepstrum; Clustering algorithms; Data mining; Feature extraction; Neural networks; Partitioning algorithms; Principal component analysis; Robustness; System testing; Vector quantization;
Conference_Titel :
Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on
Print_ISBN :
953-184-061-X
DOI :
10.1109/ISPA.2003.1296947
