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

Chong Yaw Wee; P. Raveendran; Fumiaki Takeda; Takeo Tsuzuki; Hiroshi Kadota; Satoshi Shimanouchi

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

Chong Yaw Wee, P. Raveendran, Fumiaki Takeda, Takeo Tsuzuki, Hiroshi Kadota, Satoshi Shimanouchi

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	1013-1018
Number of pages	6
Publication status	Published - 2002
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2002 - Honolulu, United States of America Duration: 12 May 2002 → 17 May 2002 https://ieeexplore.ieee.org/xpl/conhome/7877/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2002
Abbreviated title	IJCNN 2002
Country/Territory	United States of America
City	Honolulu
Period	12/05/02 → 17/05/02
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7877/proceeding (Proceedings)

Cite this

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title = "Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain",

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.",

author = "Wee, {Chong Yaw} and P. Raveendran and Fumiaki Takeda and Takeo Tsuzuki and Hiroshi Kadota and Satoshi Shimanouchi",

year = "2002",

language = "English",

pages = "1013--1018",

note = "IEEE International Joint Conference on Neural Networks 2002, IJCNN 2002 ; Conference date: 12-05-2002 Through 17-05-2002",

url = "https://ieeexplore.ieee.org/xpl/conhome/7877/proceeding",

}

Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain. / Wee, Chong Yaw; Raveendran, P.; Takeda, Fumiaki et al.
2002. 1013-1018 Paper presented at IEEE International Joint Conference on Neural Networks 2002, Honolulu, Hawaii, United States of America.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain

AU - Wee, Chong Yaw

AU - Raveendran, P.

AU - Takeda, Fumiaki

AU - Tsuzuki, Takeo

AU - Kadota, Hiroshi

AU - Shimanouchi, Satoshi

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

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M3 - Paper

AN - SCOPUS:0036073111

SP - 1013

EP - 1018

T2 - IEEE International Joint Conference on Neural Networks 2002

Y2 - 12 May 2002 through 17 May 2002

ER -

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

Abstract

Conference

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Cite this