Detection of comic characters in comic books and manga

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17/10/14 - christophe-rigaud.com Detection of comic characters in comic books and manga

description

State of the art methods for comic character (people) localization in comic book images and manga.

Transcript of Detection of comic characters in comic books and manga

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Detection of comic characters in comic books and manga

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Presentation

Advisers● Jean-Christophe Burie & Jean-Marc Ogier (L3i, La Rochelle, France)

● Dimosthenis Karatzas (CVC, Barcelona, Spain)

eBDtheque project

● Comic book image processing and understanding

● Cultural heritage in many countries

● Paper ↔ digital comic book conversion enhancement

● New usages using the new technologies

Applications

● Text/image search, augmented reading, reflowing...

France

Spain

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Researchers

What researchers can do for the paper to digital transition?

Digital world Digital world

Paper world

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Researchers

Image credits: contributors of the eBDtheque dataset

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Balloon classification

Speaker localization

Text timestamps

Tone and pitch

Scene understanding

Layout analysis

Panel retrieval

Reflowing

De-hyphenation

Speech synthesis

Language analysis

Translation assistance

Pose retrieval

Facial expression

Relationship retrieval

Character localization and recognition

Researchers

PANELSPANELS

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Balloon classification

Speaker localization

Text timestamps

Tone and pitch

Scene understanding

Layout analysis

Panel retrieval

Reflowing

De-hyphenation

Speech synthesis

Language analysis

Translation assistance

Pose retrieval

Facial expression

Relationship retrieval

Character localization and recognition

Researchers

TEXTTEXT

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Balloon classification

Speaker localization

Text timestamps

Tone and pitch

Scene understanding

Layout analysis

Panel retrieval

Reflowing

De-hyphenation

Speech synthesis

Language analysis

Translation assistance

Pose retrieval

Facial expression

Relationship retrieval

Character localization and recognition

Researchers

BALLOONSBALLOONS

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Balloon classification

Speaker localization

Text timestamps

Tone and pitch

Scene understanding

Layout analysis

Panel retrieval

Reflowing

De-hyphenation

Speech synthesis

Language analysis

Translation assistance

Pose retrieval

Facial expression

Relationship retrieval

Character localization and recognition

Researchers

CHARACTERSCHARACTERS

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Challenges of comics analysis

● Variation of comic book styles

● Printing & scanning method changes over time (press, ink-jet, computer-based)

● Diversity of information to extract

● A lot of semantic relations to retrieve (element's relationships, text meaning)

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Summary of comic character extraction

● Supervised approaches

● Character spotting given an example

● Unsupervised approaches

● Hypothesis from speech balloon tail position and orientation

● Region of interest from simple object removal

● Graph­based approach (Nam)

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Summary of comic character extraction

● Supervised approaches

● Character spotting given an example

● Unsupervised approaches

● Hypothesis from speech balloon tail position and orientation

● Region of interest from simple object removal

● Graph­based approach (Nam)

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Character spotting given an example [5]

● Aim: retrieve all the instances (apparitions) of a given character in all the pages of a comic book title/album/collection

● Method summary:

1) Reduction of the number of colors

2) Get the colors of the query example (user interaction)

3) Compute the user query descriptor

4) Spot similar character instances

[5] C. Rigaud, D. Karatzas, J-C Burie, J-M Ogier Color descriptor for content-based drawing retrieval DAS'14

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1) Color reduction/quantization

N>10000 N=256 N=32

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2) User query

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3) Color selection: occurrence level

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3) Color selection: occurrence + discriminality levels

Top 3 colors

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User input

Image credits: (Prunelle, la fille du cyclope, Ankama, 2010

Descriptor

4) Character spotting: example

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correct detections wrong detections

4) Character spotting: example

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Conclusions

● Contributions

● Content Based Drawing Retrieval

● Color descriptor robust to scale, rotation, translation, deformation and occlusion

● Benefit of redundant information

● Limitations

● Colored comics

● Number of color (quantization)

● Requires one example of each character

Query Correct Wrong

Result examples

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Summary of comic character extraction

● Supervised approaches

● Character spotting given an example

● Unsupervised approaches

● Hypothesis from speech balloon tail position and orientation

● Region of interest from simple object removal

● Graph­based approach (Nam)

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Hypothesis from speech balloon tail position and direction

● Given balloons in a panel, where are the (speaking) characters?

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Example

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Example

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Conclusions

● Preliminary results

● Highly relies on the quality of the tail detection

● Only for “speaking” characters (others can be spotted)

● Implicitly retrieves the relationship between balloon and character

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Summary of comic character extraction

● Supervised approaches

● Character spotting given an example

● Unsupervised approaches

● Hypothesis from speech balloon tail position and orientation

● Region of interest from simple object removal

● Graph­based approach (Nam)

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Region of interest (ROI) from simple object removal

● Given an image from a double page of manga, can you predict where the characters are?

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Region of interest (ROI) from simple object removal

● Given an image from a double page of manga, can you predict where the characters are?

??

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Region of interest (ROI) from simple object removal

● Given an image from a double page of manga, can you predict where the characters are?

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42% correct

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Image credits: Yasuhisa Hara, "Kingdom", Shueisha, since 2006.

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Region of interest (ROI) from simple object removal

● In the same image, knowing the position of panels and balloons, can you predict where the comic characters are?

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Region of interest (ROI) from simple object removal

● Knowing the position of panels and balloons, can you predict where the comic characters are?

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?

?

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Region of interest (ROI) from simple object removal

● Et voilà !

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?? 83% correct

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A first test from automatic panels and balloons extraction...

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Original image

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Image masked

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Image masked + distance transform

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To be continued...

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Graph-based approach

● Presented by Nam Le Thanh 

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Live demo: real time panels and balloons extraction

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https://github.com/crigaud/publication/tree/master/2014/TALK/intelligent_media_processing_group