ID | 113248 |
Author |
Matsumoto, Kazuyuki
Tokushima University
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Fujisawa, Akira
Aomori University
Yoshida, Minoru
Tokushima University
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Kita, Kenji
Tokushima University
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
|
Keywords | ASCII art
deep neural networks
classification
image feature
character feature
|
Content Type |
Journal Article
|
Description | 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.
|
Journal Title |
Journal of Software
|
ISSN | 1796217X
|
Publisher | Academy Publisher
|
Volume | 13
|
Issue | 10
|
Start Page | 559
|
End Page | 572
|
Published Date | 2018-10
|
Rights | Open Access Journal
|
EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
|
TextVersion |
Publisher
|
departments |
Science and Technology
|