Image Aesthetic Assessment: A Comparative Study of Hand-Crafted Deep Learning Models

Anwar, A; Kanwal, S; Tahir, M; Saqib, M; Uzair, M; Rahmani, MKI; Ullah, H

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted Deep Learning Models

Anwar, A Kanwal, S Tahir, M Saqib, M

Uzair, M Rahmani, MKI Ullah, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2022, 10, pp. 101770-101789
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Anwar, A
dc.contributor.author	Kanwal, S
dc.contributor.author	Tahir, M
dc.contributor.author	Saqib, M https://orcid.org/0000-0003-4374-0888
dc.contributor.author	Uzair, M
dc.contributor.author	Rahmani, MKI
dc.contributor.author	Ullah, H
dc.date.accessioned	2023-05-25T01:49:04Z
dc.date.available	2023-05-25T01:49:04Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Access, 2022, 10, pp. 101770-101789
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/170442
dc.description.abstract	Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/access.2022.3209196
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Image Aesthetic Assessment: A Comparative Study of Hand-Crafted Deep Learning Models
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-05-25T01:49:02Z
pubs.publication-status	Published
pubs.volume	10

Abstract:

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.

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http://hdl.handle.net/10453/170442