徳島大学 教育・研究者情報データベース(EDB)

1. (英) Akira Fujisawa / (日) 藤澤 日明 / (読) ふじさわ あきら

   役割	任意
   貢献度	任意	70
   学籍番号	推奨

2. 松本 和幸([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

   役割	任意	共著
   貢献度	任意	15
   学籍番号	推奨

3. (英) Ohta Kazuki / (日) 太田 万稀 / (読) おおた かずき

   役割	任意
   貢献度	任意	5
   学籍番号	推奨	****

4. 吉田 稔([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

   役割	任意
   貢献度	任意	5
   学籍番号	推奨

5. 北 研二

   役割	任意
   貢献度	任意	5
   学籍番号	推奨

(英) ASCII Art Classification Model by Transfer Learning and Data Augmentation

(英) In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also usedVGG16, ResNet-50, Inception v3, and Xceptions pre-trained networks to fine tune our categorization. As a result of the experiment of fine tuning by VGG16and data augmentation, an accuracy rate of 80% or more was obtained in the human category.

1. (英) Text art / (日) テキストアート
2. (英) ASCII art / (日) アスキーアート
3. (英) transfer learning / (日) 転移学習
4. (英) fine tuning / (日) ファインチューニング
5. (英) data augmentation / (日) データ拡張