mlsanityData to 14 Oct 2026

Sketch-based Manga Retrieval using Manga109 Dataset

arXiv 1510.04389 ↗First posted 15 Oct 2015cs.CV cs.IR cs.MMcited by 329

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Abstract (arXiv)

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, including keyword-based search by title or author, or tag-based categorization. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a content-based manga retrieval system. First, we propose a manga-specific image-describing framework. It consists of efficient margin labeling, edge orientation histogram feature description, and approximate nearest-neighbor search using product quantization. Second, we propose a sketch-based interface as a natural way to interact with manga content. The interface provides sketch-based querying, 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. We conducted a comparative study, a localization evaluation, and a large-scale qualitative study. From the experiments, we verified that: (1) the retrieval accuracy of the proposed method is higher than those of previous methods; (2) the proposed method can localize an object instance with reasonable runtime and accuracy; and (3) sketch querying is useful for manga search.

Key facts. Cited by 329 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Oct 2015.

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Citing papers per year: 2021: 30; 2022: 68; 2023: 61; 2024: 80; 2025: 62; 2026: 28.

329 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
  3. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
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  5. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 352
  6. 2023DiffBIR: Towards Blind Image Restoration with Generative Diffusion Priorcited by 257
  7. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 255
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  9. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  10. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  11. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  12. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 109
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  1. 2024MambaIRv2: Attentive State Space Restorationcited by 94
  2. 2022Cross Aggregation Transformer for Image Restorationcited by 92
  3. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 92
  4. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
  5. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  6. 2024Arbitrary-steps Image Super-resolution via Diffusion Inversioncited by 86
  7. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  8. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  9. 2022CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolutioncited by 70
  10. 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 69
  11. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 66
  12. 2023CoSeR: Bridging Image and Language for Cognitive Super-Resolutioncited by 66
  13. 2021On Efficient Transformer-Based Image Pre-training for Low-Level Visioncited by 64
  14. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 64
  15. 2024Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionarycited by 64
  16. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  17. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  18. 2024A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trendscited by 60
  19. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 57
  20. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 57
  21. 2023A Comparative Study of Image Restoration Networks for General Backbone Network Designcited by 57
  22. 2022Reference-based Image Super-Resolution with Deformable Attention Transformercited by 51
  23. 2025Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modelingcited by 48
  24. 2021AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsamplingcited by 47
  25. 2022Deep Constrained Least Squares for Blind Image Super-Resolutioncited by 47
  26. 2022N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolutioncited by 46
  27. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 45
  28. 2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 45
  29. 2022Blueprint Separable Residual Network for Efficient Image Super-Resolutioncited by 44
  30. 2024HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolutioncited by 42
  31. 2021Reflash Dropout in Image Super-Resolutioncited by 41
  32. 2024DRCT: Saving Image Super-resolution away from Information Bottleneckcited by 40
  33. 2022Revisiting RCAN: Improved Training for Image Super-Resolutioncited by 32
  34. 2021Best-Buddy GANs for Highly Detailed Image Super-Resolutioncited by 30
  35. 2021A Practical Contrastive Learning Framework for Single-Image Super-Resolutioncited by 30
  36. 2024CAMixerSR: Only Details Need More "Attention"cited by 30
  37. 2022Multi-scale Attention Network for Single Image Super-Resolutioncited by 29
  38. 2023Recursive Generalization Transformer for Image Super-Resolutioncited by 29

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