Adaptation of manga face representation for accurate clustering

Autor: Toru Ogawa, Koki Tsubota, Toshihiko Yamasaki, Kiyoharu Aizawa
Rok vydání: 2018
Předmět:
Zdroj: SIGGRAPH ASIA Posters
DOI: 10.1145/3283289.3283319
Popis: Manga character drawing styles differ greatly among artists. To accurately cluster faces within an individual manga, we propose a method to adapt manga face representations to an individual manga. We use deep features trained for generic manga face recognition, and adapt them by deep metric learning (DML) for the target manga volume. DML uses pseudo positive and negative pairs defined by considering page and frame information. We performed experiments using a dataset comprising 104 manga volumes and found that our feature adaptation significantly improved the accuracy of manga face clustering.
Databáze: OpenAIRE