文章基本信息

标题：ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters
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作者：Kazuyuki Matsumoto ; Akira Fujisawa ; Minoru Yoshida 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2018
卷号：13
期号：10
页码：559-572
DOI：10.17706/jsw.13.10.559-572
出版社：Academy Publisher
摘要：In recent years, a lot of non-verbal expressions have been used on social media.Ascii art (AA) is an expression using characters withvisual technique. In this paper, we set up an experiment to classify AA pictures by using character features and image features.We try to clarifywhich feature is more effective for amethod to classify AA pictures.We proposed fivemethods: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 networksand5) a method based on image features of characters.We trained neural networks by using these fivefeatures.Inthe experimental result, the best classification accuracy was obtained inthe feed forward neural networks that usedimage features of characters.
其他关键词：ASCII art, deep neural networks, classification, image feature, character feature