ASCII Art Classification Model by Transfer Learning and Data Augmentation
ASCII Art Classification Model by Transfer Learning and Data Augmentation
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DOI：
10.3233/faia200738
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发表时间：
2020-11
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通讯作者：
Akira Fujisawa;Kazuyuki Matsumoto;Kazuki Ohta;Minoru Yoshida;K. Kita
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作者：
Akira Fujisawa;Kazuyuki Matsumoto;Kazuki Ohta;Minoru Yoshida;K. Kita
在这项研究中，我们提出了一种基于迁移学习和数据增强的ASCII艺术类别分类方法。ASCII艺术是一种非语言的表达形式，可以在视觉上表达情感和意图。虽然也有类似的表达式，如表情符号和象形图，但大多数都是由单个字符表示的，或者作为内联表达式嵌入到语句中。ASCII艺术以各种风格表达，包括点艺术插图和线艺术插图。基本上，ASCII艺术可以代表几乎任何对象，因此ASCII艺术的类别非常多样化。许多现有的图像分类算法使用颜色信息;然而，由于大多数ASCII艺术是用字符集编写的，因此没有颜色信息可用于分类。我们使用灰度边缘图像和从图像转换的ASCII艺术图像作为训练图像集创建了ASCII艺术类别分类器。我们还使用VGG 16、ResNet-50、Inception v3和Xception的预训练网络来微调我们的分类。通过VGG 16的微调和数据扩充实验，在“人类”类别中获得了80%以上的准确率。
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 used VGG16, ResNet-50, Inception v3, and Xception’s pre-trained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy rate of 80% or more was obtained in the “human” category.