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 conferencePaperpeer-review

4 Citations (Scopus)


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 languageEnglish
Number of pages6
Publication statusPublished - 2002
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2002 - Honolulu, United States of America
Duration: 12 May 200217 May 2002
https://ieeexplore.ieee.org/xpl/conhome/7877/proceeding (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2002
Abbreviated titleIJCNN 2002
Country/TerritoryUnited States of America
Internet address

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