Similar Partial Copy Detection of Line Drawings Using a Cascade Classifier and Feature Matching

Sun, Weihan; Kise, Koichi

doi:10.1007/978-3-642-19376-7_11

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…HOG (Histogram of Oriented Gradients) [4] is used for obtaining local feature vectors from local feature regions obtained above. HOG is superior to SIFT in viewpoint of the performance to retrieve a corresponding image to a similar query image, for example, hand-drawn copy [2]. Figure 2 shows the process of obtaining HOG features, that is, local feature vector.…”

Section: Sun's Methodsmentioning

confidence: 98%

“…However the realization of manga character retrieval method is not so easy because there are various expressions and poses of the same manga character. Sun et.al. proposed the similar image retrieval method for detecting printed or hand-drawn partial copies of line drawings [1], [2]. The method employed a voting procedure by matching local feature vectors between a query and each image in a database.…”

Section: Introductionmentioning

confidence: 99%

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A Study to Achieve Manga Character Retrieval Method for Manga Images

Iwata

Ito

Kise

2014

2014 11th IAPR International Workshop on Document Analysis Systems

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Manga (Japanese style comics) is one of the most popular publications. Nowadays manga is often handled as digital images not only in consumers' use but also in digital media. However, they hardly handle manga as content-based materials. Some digital media use tags or text data for retrieval, where the tags and text data are produced by handmade input. Therefore our goal is achieving content-based retrieval method for manga images. As the first step to the goal, we investigate the performance of Sun's method applying to manga character retrieval. Manga character retrieval means a image retrieval of which the input and output are a character image and page images where the input character appears respectively. It is useful for convenient use of manga images, for example, character retrieval or auto-tagging. We modify Sun's method so as to be applicable to manga character retrieval and then investigate the performance.

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Section: Sun's Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A Study to Achieve Manga Character Retrieval Method for Manga Images

Iwata

Ito

Kise

2014

2014 11th IAPR International Workshop on Document Analysis Systems

Self Cite

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“…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 main contribution of this work is as follows: Firstly, we introduce a new dataset on Bengali Comic book pages which includes various kinds of structural layouts and huge variations of comic characters. We annotate the entire character because only face regions of comic character are not distinguishable enough to identify characters further [19]. Secondly, proposing a panel/character localization architecture based on the features of YOLO and CNN.…”

Section: Introductionmentioning

confidence: 99%

CNN Based Extraction of Panels/Characters from Bengali Comic Book Page Images

Dutta

Biswas

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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Peoples nowadays prefer to use digital gadgets like cameras or mobile phones for capturing documents. Automatic extraction of panels/characters from the images of a comic document is challenging due to the wide variety of drawing styles adopted by writers, beneficial for readers to read them on mobile devices at any time and useful for automatic digitization. Most of the methods for localization of panel/character rely on the connected component analysis or page background mask and are applicable only for a limited comic dataset. This work proposes a panel/character localization architecture based on the features of YOLO and CNN for extraction of both panels and characters from comic book images. The method achieved remarkable results on Bengali Comic Book Image dataset (BCBId) consisting of total 4130 images, developed by us as well as on a variety of publicly available comic datasets in other languages, i.e. eBDtheque, Manga 109 and DCM dataset.

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Similar Partial Copy Detection of Line Drawings Using a Cascade Classifier and Feature Matching

Cited by 7 publications

References 5 publications

A Study to Achieve Manga Character Retrieval Method for Manga Images

A Study to Achieve Manga Character Retrieval Method for Manga Images

Digital Comics Image Indexing Based on Deep Learning

CNN Based Extraction of Panels/Characters from Bengali Comic Book Page Images

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