© Gasan

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, i.e., keyword-based search by title or author. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a manga-specific image retrieval system. The proposed system consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization. For querying, the system provides a sketch-based interface. Based on the interface, two interactive reranking schemes are presented: relevance feedback and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. Experimental results showed that the proposed framework is efficient and scalable (70 ms from 21,142 pages using a single computer with 204 MB RAM).

• Sketch-based Manga Retrieval using Manga109 Dataset
  Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, Kiyoharu Aizawa
  Multimedia Tools and Applications (MTAP), 2017 pdf
• Challenge for Manga Processing: Sketch-based Manga Retrieval
  Yusuke Matsui
  ACM International Conference on Multimedia (ACMMM), Doctoral Symposium, 2015
• Sketch2Manga: Sketch-based Manga Retrieval
  Yusuke Matsui, Kiyoharu Aizawa, Yushi Jing
  IEEE International Conference on Image Processing (ICIP), 2014 pdf

Manga109 dataset

• 21,142 pages, 94 authors, 109 manga titles.
• Drawn by professional manga artists beween the 1970s to the 2010s.
• Publicly availabe for academic research purposes with proper copyright notation.

@article{mtap_matsui_2017,
    author={Yusuke Matsui and Kota Ito and Yuji Aramaki and Azuma Fujimoto and Toru Ogawa and Toshihiko Yamasaki and Kiyoharu Aizawa},
    title={Sketch-based Manga Retrieval using Manga109 Dataset},
    journal={Multimedia Tools and Applications},
    volume={76},
    number={20},
    pages={21811--21838},
    year={2017}
}