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https://ir.swu.ac.th/jspui/handle/123456789/12269
Title: | Open-set bottle classifying using a convolution neural network |
Authors: | Jintawatsakoon S. Charoenruengkit W. |
Keywords: | Bottles Convolution Knowledge engineering Beverage bottles Combination models Convolution neural network Input image Real-world Traditional approaches Training time Image classification |
Issue Date: | 2019 |
Abstract: | A multi-class image classification application plays a vital role in our lives. Traditional approaches focus on a close-set classification problem. However, an open-set classification problem often occur in the real-world applications. This paper focuses on the convolution neural network based image classification for beverage bottle image classification under the open-set environment, in which the input image may not appear in any known classes during training time. The proposed models explore the approaches based on the N-Binary, N+unknown, and N+combination models. The results show that N+unknown approach perform better than that of the N+combination and N-Binary approach in terms of accuracy and time. © 2019 IEEE. |
URI: | https://ir.swu.ac.th/jspui/handle/123456789/12269 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078988653&doi=10.1109%2fICTKE47035.2019.8966900&partnerID=40&md5=1c5cdd2d447b8bd7280f60c5cd533a4f |
ISSN: | 21570981 |
Appears in Collections: | Scopus 1983-2021 |
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