Series Photo Selection via Multi-View Graph Learning

Huang, J; Zhang, L; Gong, Y; Zhang, J; Nie, X; Yin, Y

Series Photo Selection via Multi-View Graph Learning

Huang, J Zhang, L Gong, Y Zhang, J

Nie, X Yin, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, 2022-July, pp. 01-06
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Huang, J
dc.contributor.author	Zhang, L
dc.contributor.author	Gong, Y
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Nie, X
dc.contributor.author	Yin, Y
dc.date	2022-07-18
dc.date.accessioned	2023-04-12T23:00:21Z
dc.date.available	2023-04-12T23:00:21Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, 2022-July, pp. 01-06
dc.identifier.isbn	9781665485630
dc.identifier.issn	1945-7871
dc.identifier.issn	1945-788X
dc.identifier.uri	http://hdl.handle.net/10453/169669
dc.description.abstract	Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. While a great progress has been observed, most of the existing SPS approaches concentrate solely on extracting features from the original image, neglecting that multiple views, e.g, saturation level, color histogram and depth of field of the image, will be of benefit to successfully reflecting the subtle aesthetic changes. Taken multi-view into consideration, we leverage a graph neural network to construct the relationships between multi-view features. Besides, multiple views are aggregated with an adaptive-weight self-attention module to verify the significance of each view. Finally, a siamese network is proposed to select the best one from a series of nearly identical photos. Experimental results demonstrate that our model accomplish the highest success rates compared with competitive methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 IEEE International Conference on Multimedia and Expo (ICME)
dc.relation.ispartof	2022 IEEE International Conference on Multimedia and Expo
dc.relation.isbasedon	10.1109/icme52920.2022.9859645
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Series Photo Selection via Multi-View Graph Learning
dc.type	Conference Proceeding
utslib.citation.volume	2022-July
utslib.location.activity	Taipei, Taiwan
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-12T23:00:19Z
pubs.finish-date	2022-07-22
pubs.publication-status	Published
pubs.start-date	2022-07-18
pubs.volume	2022-July

Abstract:

Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. While a great progress has been observed, most of the existing SPS approaches concentrate solely on extracting features from the original image, neglecting that multiple views, e.g, saturation level, color histogram and depth of field of the image, will be of benefit to successfully reflecting the subtle aesthetic changes. Taken multi-view into consideration, we leverage a graph neural network to construct the relationships between multi-view features. Besides, multiple views are aggregated with an adaptive-weight self-attention module to verify the significance of each view. Finally, a siamese network is proposed to select the best one from a series of nearly identical photos. Experimental results demonstrate that our model accomplish the highest success rates compared with competitive methods.

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http://hdl.handle.net/10453/169669