Analysis Based on Distributed Representations of Various Parts Images in Four-Scene Comics Story Dataset

Ways of understanding human creations with artificial intelligence have increased; however, those are still known as one of the most difficult tasks. Our challenge is to find ways to understand four-scene comics by AI. To achieve this aim, we used the novel dataset called"Four-scene Comics Story Dataset", which is the first dataset made by researchers and comic artists to develop AI creations. We focused on illustration touches of comics which are determined by comic artists. First, we applied convolutional autoencoder (CAE) models to this dataset to get distributed representations, then applied classifiers in order to classify the different touches. The prediction offers an indirect measure of the distributed representations. We already investigated the experiment using original scenes. To show the advantageous of the dataset, the proposed method is confirmed by computer simulations taking data of various pattern of removing image parts such as "eyes" in scene images of the four-scene comics story dataset structure as examples.