Ensemble image classification method based on genetic image network

Authors:
Shiro Nakayama;Shinichi Shirakawa;Noriko Yata;Tomoharu Nagao
Affiliations:
Graduate School of Environment and Information Sciences, Yokohama National University, Kanagawa, Japan;Graduate School of Environment and Information Sciences, Yokohama National University, Kanagawa, Japan;Graduate School of Environment and Information Sciences, Yokohama National University, Kanagawa, Japan;Graduate School of Environment and Information Sciences, Yokohama National University, Kanagawa, Japan
Venue:
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Year:
2010

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Abstract

Automatic construction method for image classification algorithms have been required. Genetic Image Network for Image Classification (GIN-IC) is one of the methods that construct image classification algorithms automatically, and its effectiveness has already been proven. In our study, we try to improve the performance of GIN-IC with AdaBoost algorithm using GIN-IC as weak classifiers to complement with each other. We apply our proposed method to three types of image classification problems, and show the results in this paper. In our method, discrimination rates for training images and test images improved in the experiments compared with the previous method GIN-IC.