A Classification Method for “Kawaii” Images Using Semantic Interpretation

This paper proposes a classification method for five types of images represented by the word “kawaii.” “kawaii images” do not have fixed concept or object, which makes it to classify them simply using conventional methods such as Convolutional Neural Network or Support Vector Machine. Our previous study extracted color and shape features from such images, then achieving a classification accuracy of 70.2 %. However, this approach did not handle the semantic content of the images. In this study, a classification method based on the constituent elements of “kawaii images” is used, resulting in a classification accuracy of 71.4 %. Additionally, the experiment seems to providing possibilities to reflect similarities with human recognition to some extent.