Progressive Deep Feature Learning for Manga Character Recognition via Unlabeled Training Data

摘要：

　　The recognition of manga(Japanese comics)characters is an essen-tial step in industrial applications,such as manga character retrieval,content analysis and copyright protection.However,conventional methods for manga character recognition are mainly based on hand-crafted features which are not robust enough for manga of various style.The emergence of deep learning based methods provides representational features,which has a huge demand for labeled data.In this paper,we propose a framework to exploit unlabeled manga data to facilitate the discriminative capability of deep feature representations for manga character recognition(i.e.,unsupervised learning on manga images),which does not rely on any manual annotation.Specifically,we first train an initial feature model using an anime character dataset.Then,we adopt a Progressive Main Characters Mining(PMCM)strategy which iterates between two steps:1)produce selected data with estimated labels from unlabeled data,2)update the feature model by the selected data.These two steps are mutually promoted in essence.Experimental results on Manga1 09 dataset,to which we introduce new head annotations,demonstrate the effectiveness of the proposed framework and the usefulness in manga character verification and retrieval.

关键词： manga images manga character recognition unsupervised learning label estimation

作者: Xiaoran Qin Yafeng Zhou Yonggang Li Siwei Wang Yongtao Wang Zhi Tang

作者单位: Peking University Beijing,China Beijing University of Posts and Telecommunications Beijing,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 169-174

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）