Title :
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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005614
