Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김영민 | - |
dc.date.accessioned | 2020-06-03T06:40:33Z | - |
dc.date.available | 2020-06-03T06:40:33Z | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | Journal of Big Data, v. 6, article no. 56 | en_US |
dc.identifier.issn | 2196-1115 | - |
dc.identifier.uri | https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0222-3 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/151434 | - |
dc.description.abstract | Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope of the applications to include artworks. However, popular art forms, such as comics, have been ignored in this trend. This study investigates visual features for comic classification using deep neural networks. An effective input format for comic classification is first defined, and a convolutional neural network is used to classify comic images into eight different artist categories. Using a publicly available dataset, the trained model obtains a mean F1 score of 84\% for the classification. A feature visualization technique is also applied to the trained classifier, to verify the internal visual characteristics that succeed in classification. The experimental result shows that the visualized features are significantly different from those of general object classification. This work represents one of the first attempts to examine the visual characteristics of comics using feature visualization, in terms of comic author classification with deep neural networks. | en_US |
dc.description.sponsorship | This work is partially supported by two projects, Classification of The Artists using Deep Neural Networks, funded by Hanyang University (201600000002255) and Smart Multimodal Environment of AI Chatbot Robots for Digital Healthcare (P0000536), funded by the Ministry of Trade, Industry and Energy (MOTIE). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Open | en_US |
dc.subject | Artistic styles | en_US |
dc.subject | Comic classification | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | Feature visualization | en_US |
dc.title | Feature Visualization in Comic Artist Classification using Deep Neural Networks | en_US |
dc.type | Article | en_US |
dc.relation.no | 56 | - |
dc.relation.volume | 6 | - |
dc.identifier.doi | 10.1186/s40537-019-0222-3 | - |
dc.relation.page | 1-18 | - |
dc.relation.journal | Journal of Big Data | - |
dc.contributor.googleauthor | Kim, Young-Min | - |
dc.relation.code | 2019021253 | - |
dc.sector.campus | S | - |
dc.sector.daehak | GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S] | - |
dc.sector.department | DEPARTMENT OF TECHNOLOGY MANAGEMENT | - |
dc.identifier.pid | yngmnkim | - |
dc.identifier.orcid | https://orcid.org/0000-0002-6914-901X | - |
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