Image recognition systems with permutative coding

Author

Kussul, E. ; Baidyk, T. ; Wunsch, D.C., II

Author_Institution

Center of Appl. Sci. & Technol. Dev., National Autonomous Univ. of Mexico, Mexico

Volume

3

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

1788

Abstract

A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%.

Keywords

feature extraction; handwriting recognition; image coding; image recognition; neural nets; object recognition; visual databases; MNIST database; ORL database; face recognition; feature extractor; handwritten digit recognition; image recognition system; microobjects recognition; neural classifier; neural network; permutative coding; Error analysis; Face recognition; Feature extraction; Handwriting recognition; Image coding; Image databases; Image recognition; Neural networks; Shape; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1556151

Filename

1556151