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Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships.

Ziwen Lan1, Keisuke Maeda2, Takahiro Ogawa2

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

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Summary

This study introduces a hierarchical graph convolutional network (GCN) model for classifying multiple attributes in anime illustrations. The model effectively captures attribute relationships and creator-intended details for improved accuracy.

Keywords:
anime illustrationattribute classificationgenerative adversarial networksgraph convolutional networkshierarchical classification

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Anime illustration attribute classification is complex due to subtle, creator-intended features.
  • Existing methods struggle to capture hierarchical and co-occurrence relationships among attributes.

Purpose of the Study:

  • To propose a novel hierarchical multi-modal multi-label attribute classification model for anime illustrations.
  • To enhance the accuracy of attribute classification by leveraging hierarchical structures and graph convolutional networks.

Main Methods:

  • Developed a graph convolutional network (GCN) model incorporating hierarchical clustering and label assignments.
  • Organized attribute information into a hierarchical feature to represent subordinate relationships.
  • Constructed a hierarchical attribute structure based on frequency and derived rules.

Main Results:

  • The proposed GCN-based model achieved high accuracy in multi-label attribute classification.
  • Demonstrated effective capture of attribute co-occurrence and subordinate relationships.
  • Experimental results show superiority over existing methods on multiple datasets.

Conclusions:

  • The hierarchical GCN model is effective and extensible for anime illustration attribute classification.
  • Integrating hierarchical structures significantly improves the model's ability to understand complex attribute relationships.
  • The method offers a promising approach for detailed content analysis in visual media.