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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jintawatsakoon S. | |
dc.contributor.author | Charoenruengkit W. | |
dc.date.accessioned | 2021-04-05T03:02:28Z | - |
dc.date.available | 2021-04-05T03:02:28Z | - |
dc.date.issued | 2019 | |
dc.identifier.issn | 21570981 | |
dc.identifier.other | 2-s2.0-85078988653 | |
dc.identifier.uri | https://ir.swu.ac.th/jspui/handle/123456789/12269 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078988653&doi=10.1109%2fICTKE47035.2019.8966900&partnerID=40&md5=1c5cdd2d447b8bd7280f60c5cd533a4f | |
dc.description.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. | |
dc.subject | Bottles | |
dc.subject | Convolution | |
dc.subject | Knowledge engineering | |
dc.subject | Beverage bottles | |
dc.subject | Combination models | |
dc.subject | Convolution neural network | |
dc.subject | Input image | |
dc.subject | Real-world | |
dc.subject | Traditional approaches | |
dc.subject | Training time | |
dc.subject | Image classification | |
dc.title | Open-set bottle classifying using a convolution neural network | |
dc.type | Conference Paper | |
dc.rights.holder | Scopus | |
dc.identifier.bibliograpycitation | International Conference on ICT and Knowledge Engineering. Vol 2019-November | |
dc.identifier.doi | 10.1109/ICTKE47035.2019.8966900 | |
Appears in Collections: | Scopus 1983-2021 |
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