Publication: Open-set bottle classifying using a convolution neural network
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Issued Date
2019
Resource Type
File Type
application/pdf
ISSN
21570981
Other identifier(s)
2-s2.0-85078988653
Rights Holder(s)
Scopus
Bibliographic Citation
International Conference on ICT and Knowledge Engineering. Vol 2019-November
Suggested Citation
Jintawatsakoon S., Charoenruengkit W. Open-set bottle classifying using a convolution neural network. International Conference on ICT and Knowledge Engineering. Vol 2019-November. doi:10.1109/ICTKE47035.2019.8966900 Retrieved from: https://hdl.handle.net/20.500.14740/5100
Author(s)
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.