ID | 113248 |
著者 |
Fujisawa, Akira
Aomori University
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キーワード | ASCII art
deep neural networks
classification
image feature
character feature
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資料タイプ |
学術雑誌論文
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抄録 | In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expression using characters with visual technique. In this paper, we set up an experiment to classify AA pictures by using character features and image features. We try to clarify which feature is more effective for a method to classify AA pictures. We proposed four methods: 1) a method based on character frequency, 2) a method based on character importance value and 3) a method based on image features, 4) a method based on image features using pre-trained neural networks and 5) a method based on image features of characters. We trained neural networks by using these five features. In the experimental result, the best classification accuracy was obtained in the feed forward neural networks that used image features of characters.
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掲載誌名 |
Journal of Software
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ISSN | 1796217X
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出版者 | Academy Publisher
|
巻 | 13
|
号 | 10
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開始ページ | 559
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終了ページ | 572
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発行日 | 2018-10
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権利情報 | Open Access Journal
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EDB ID | |
出版社版DOI | |
出版社版URL | |
フルテキストファイル | |
言語 |
eng
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著者版フラグ |
出版社版
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部局 |
理工学系
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