Detection of exact and similar partial copies for copyright protection of manga

Sun, Weihan; Kise, Koichi

doi:10.1007/s10032-013-0199-y

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…Several studies have explored manga retrieval [59], [60], [61], [62]. In these methods, inputs are usually not pages but cropped frames or characters, and evaluations were conducted using a small number of examples.…”

Section: Mangamentioning

confidence: 99%

Sketch-based manga retrieval using manga109 dataset

Matsui

Ito

Aramaki

et al. 2016

Multimed Tools Appl

1,006

347

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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 contentbased 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 sketchbased 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.

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Section: Mangamentioning

confidence: 99%

Sketch-based manga retrieval using manga109 dataset

Matsui

Ito

Aramaki

et al. 2016

Multimed Tools Appl

1,006

347

View full text Add to dashboard Cite

show abstract

“…In [46,47], the authors proved that Viola-Jones detection framework [48] is sufficient for detecting faces in mangas (Japanese comics). However, in [49], the authors showed that prior techniques for face detection and face recognition for real people's faces (including [48]) can hardly be applied to colored comics characters because comic character faces considerably differ from real people faces in respect of organ positions, sizes and color shades.…”

Section: Comic Charactersmentioning

confidence: 99%

Digital Comics Image Indexing Based on Deep Learning

2018

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Abstract:The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.

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“…The legacy ROI algorithm can be applied to copy detection by identifying objects partially located in the ROI [17]. However, immense processing time is required to figure out objects in the ROI, which is also exponentially increased in proportion to the image size.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of (a) a color image and (b) two different comic pages[17] with corresponding histograms of pixel intensity.…”

mentioning

confidence: 99%