Japanese / English


論文の言語 英語
著者 Yuki Daiku, Motoi Iwata, Olivier Augereau, Koichi Kise
論文名 Comics Story Representation System Based on Genre
論文誌名 Proceedings of the IAPR International Workshop on Document Analysis Systems
年月 2018年4月
要約 Comics is usually classified into broad categories called “genres” according to its contents such as comedy, horror, science fiction, etc. Because a genre expresses a comics story briefly, people read comics which has contents based on their interest, by relying on comics genres. However, giving only one genre to one comic cannot express the detailed difference of the story. In this paper, we propose a system for generating comics story representation as a sub-sequence of genres. Our comics story representation can be applied to a new search engine based on stories or to a recommendation system which analyzes the tastes of the user’s favorite comics by finding comics with similar story representation. We use a deep neural network to classify each page into the corresponding genre. Experimental results confirm the advantage of the proposed system.
DOI 10.1109/ICDAR.2017.293
URL https://ieeexplore.ieee.org/document/8270238