Hunting out graphic images from real images using recurrent neural network and extended principal color components

Gahye Lee; Seungkyu Lee

doi:10.1145/3283289.3283353

Hunting out graphic images from real images using recurrent neural network and extended principal color components

Gahye Lee
, Seungkyu Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.

Original language	English
Title of host publication	SIGGRAPH Asia 2018 Posters, SA 2018
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450360630
DOIs	https://doi.org/10.1145/3283289.3283353
Publication status	Published - 4 Dec 2018
Event	SIGGRAPH Asia 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2018 - Tokyo, Japan Duration: 4 Dec 2018 → 7 Dec 2018

Publication series

Name	SIGGRAPH Asia 2018 Posters, SA 2018

Conference

Conference	SIGGRAPH Asia 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2018
Country/Territory	Japan
City	Tokyo
Period	4/12/18 → 7/12/18

Bibliographical note

Publisher Copyright:
© 2018 Copyright held by the owner/author(s).

Keywords

Graphic real image classification

Access to Document

10.1145/3283289.3283353

Cite this

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title = "Hunting out graphic images from real images using recurrent neural network and extended principal color components",

abstract = "With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.",

keywords = "Graphic real image classification",

author = "Gahye Lee and Seungkyu Lee",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; SIGGRAPH Asia 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2018 ; Conference date: 04-12-2018 Through 07-12-2018",

year = "2018",

month = dec,

day = "4",

doi = "10.1145/3283289.3283353",

language = "English",

series = "SIGGRAPH Asia 2018 Posters, SA 2018",

publisher = "Association for Computing Machinery, Inc",

booktitle = "SIGGRAPH Asia 2018 Posters, SA 2018",

}

Lee, G & Lee, S 2018, Hunting out graphic images from real images using recurrent neural network and extended principal color components. in SIGGRAPH Asia 2018 Posters, SA 2018., 3283353, SIGGRAPH Asia 2018 Posters, SA 2018, Association for Computing Machinery, Inc, SIGGRAPH Asia 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2018, Tokyo, Japan, 4/12/18. https://doi.org/10.1145/3283289.3283353

Hunting out graphic images from real images using recurrent neural network and extended principal color components. / Lee, Gahye; Lee, Seungkyu.
SIGGRAPH Asia 2018 Posters, SA 2018. Association for Computing Machinery, Inc, 2018. 3283353 (SIGGRAPH Asia 2018 Posters, SA 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lee, Seungkyu

PY - 2018/12/4

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N2 - With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.

AB - With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.

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T3 - SIGGRAPH Asia 2018 Posters, SA 2018

BT - SIGGRAPH Asia 2018 Posters, SA 2018

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T2 - SIGGRAPH Asia 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2018

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ER -

Hunting out graphic images from real images using recurrent neural network and extended principal color components

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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