Ternary classification for image aesthetic assessment using deep learning

Hyeongnam Jang; Jong Seok Lee

doi:10.1109/ICCE-Asia49877.2020.9277483

Ternary classification for image aesthetic assessment using deep learning

Hyeongnam Jang, Jong Seok Lee

School of Integrated Technology

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

Abstract

As the number of digital photos increases, the need for image aesthetic assessment is increasing in various applications to provide improved user satisfaction. Most existing studies have considered binary classification to determine whether an image has a high- or low-level of aesthetic quality. However, the binary classification has a limitation in that when an image is classified incorrectly, users experience a large gap between their perception and the predicted result. To reduce the gap, we propose ternary classification-based image aesthetic assessment. Through experiments using popular classification deep learning models, we show the advantages of the ternary classification over the binary classification.

Original language	English
Title of host publication	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728161648
DOIs	https://doi.org/10.1109/ICCE-Asia49877.2020.9277483
Publication status	Published - 2020 Nov 1
Event	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of Duration: 2020 Nov 1 → 2020 Nov 3

Publication series

Name	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Conference

Conference	2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Country/Territory	Korea, Republic of
City	Seoul
Period	20/11/1 → 20/11/3

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/ICCE-Asia49877.2020.9277483

Cite this

Jang, H., & Lee, J. S. (2020). Ternary classification for image aesthetic assessment using deep learning. In 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 Article 9277483 (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Asia49877.2020.9277483

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title = "Ternary classification for image aesthetic assessment using deep learning",

abstract = "As the number of digital photos increases, the need for image aesthetic assessment is increasing in various applications to provide improved user satisfaction. Most existing studies have considered binary classification to determine whether an image has a high- or low-level of aesthetic quality. However, the binary classification has a limitation in that when an image is classified incorrectly, users experience a large gap between their perception and the predicted result. To reduce the gap, we propose ternary classification-based image aesthetic assessment. Through experiments using popular classification deep learning models, we show the advantages of the ternary classification over the binary classification.",

keywords = "Image aesthetic assessment, deep learning, perceived error., ternary classification",

author = "Hyeongnam Jang and Lee, {Jong Seok}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 ; Conference date: 01-11-2020 Through 03-11-2020",

year = "2020",

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doi = "10.1109/ICCE-Asia49877.2020.9277483",

language = "English",

series = "2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020",

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Jang, H & Lee, JS 2020, Ternary classification for image aesthetic assessment using deep learning. in 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020., 9277483, 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020, Seoul, Korea, Republic of, 20/11/1. https://doi.org/10.1109/ICCE-Asia49877.2020.9277483

Ternary classification for image aesthetic assessment using deep learning. / Jang, Hyeongnam; Lee, Jong Seok.
2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9277483 (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020).

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

TY - GEN

T1 - Ternary classification for image aesthetic assessment using deep learning

AU - Jang, Hyeongnam

AU - Lee, Jong Seok

PY - 2020/11/1

Y1 - 2020/11/1

N2 - As the number of digital photos increases, the need for image aesthetic assessment is increasing in various applications to provide improved user satisfaction. Most existing studies have considered binary classification to determine whether an image has a high- or low-level of aesthetic quality. However, the binary classification has a limitation in that when an image is classified incorrectly, users experience a large gap between their perception and the predicted result. To reduce the gap, we propose ternary classification-based image aesthetic assessment. Through experiments using popular classification deep learning models, we show the advantages of the ternary classification over the binary classification.

AB - As the number of digital photos increases, the need for image aesthetic assessment is increasing in various applications to provide improved user satisfaction. Most existing studies have considered binary classification to determine whether an image has a high- or low-level of aesthetic quality. However, the binary classification has a limitation in that when an image is classified incorrectly, users experience a large gap between their perception and the predicted result. To reduce the gap, we propose ternary classification-based image aesthetic assessment. Through experiments using popular classification deep learning models, we show the advantages of the ternary classification over the binary classification.

KW - Image aesthetic assessment

KW - deep learning

KW - perceived error.

KW - ternary classification

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M3 - Conference contribution

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T3 - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

BT - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Y2 - 1 November 2020 through 3 November 2020

ER -

Jang H, Lee JS. Ternary classification for image aesthetic assessment using deep learning. In 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9277483. (2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020). doi: 10.1109/ICCE-Asia49877.2020.9277483

Ternary classification for image aesthetic assessment using deep learning

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