Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain

Author

Wee, Chong Yaw ; Raveendran, Paramesaran ; Takeda, Fumiaki ; Tsuzuki, Takeo ; Kadota, Hiroshi ; Shimanouchi, Satoshi

Author_Institution

Fac. of Eng., Malaya Univ., Kuala Lumpur, Malaysia

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1013

Lastpage

1018

Abstract

In this paper, Zernike moment features extracted from rice grains are used in classifying normal and damaged rice. Genetic algorithm (GA) is used to reduce the number of features while maximizing the classification performance. The GA chromosome fitness is evaluated using a multilayer perceptron (MLP) trained by backpropagation learning algorithm

Keywords

Zernike polynomials; automatic optical inspection; backpropagation; food processing industry; genetic algorithms; image classification; multilayer perceptrons; GA chromosome fitness; MLP; Zernike moments; backpropagation learning algorithm; damaged rice; feature reduction; genetic algorithm; multilayer perceptron; neural network classification; rice grain classification; Biological cells; Data mining; Feature extraction; Genetic algorithms; Genetic engineering; Genetic mutations; Image analysis; Information systems; Neural networks; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005614

Filename

1005614