Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection

Huang, J; Gong, Y; Shi, Y; Zhang, X; Zhang, J; Yin, Y

Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection

Huang, J Gong, Y Shi, Y Zhang, X Zhang, J

Yin, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2024, PP, (99), pp. 1-14
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Huang, J
dc.contributor.author	Gong, Y
dc.contributor.author	Shi, Y
dc.contributor.author	Zhang, X
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Yin, Y
dc.date.accessioned	2024-08-07T05:15:42Z
dc.date.available	2024-08-07T05:15:42Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Multimedia, 2024, PP, (99), pp. 1-14
dc.identifier.issn	1520-9210
dc.identifier.issn	1941-0077
dc.identifier.uri	http://hdl.handle.net/10453/180289
dc.description.abstract	Nowadays, capturing cherished moments results in an abundance of photos, which necessitates the selection of the finest one from a pool of akin images—a process both intricate and time-intensive. Thus, series photo selection (SPS) techniques have been developed to recommend the optimal moment from nearly identical photos through the use of aesthetic quality assessment. However, addressing SPS proves demanding due to the subtle nuances within such imagery. Existing approaches predominantly rely on diverse feature types (e.g., color, layout, generic features) extracted from original images to discern the qualified shot, yet they disregard disentangling generality and specificity at the feature level. This study aims to detect subtle aesthetic distinctions among akin photos. We propose a feature separation model that captures all label-relevant information through an encoder. We introduce Information Bottleneck (IB) learning to obtain non-redundant representations of image pairs and filter out noise information from the representations. Our model segregates image features into shared and specific attributes by employing feature constraints to boost mutual information across images and guide meaningful information within individual images. This process filters out extraneous data within individual images, thus significantly enhancing the representation of similar image pairs. Extensive experiments on the Phototriage dataset show that our model can accentuate subtle disparities and achieve superior results when compared to alternative methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Multimedia
dc.relation.isbasedon	10.1109/TMM.2024.3382469
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	recently_added	*
dc.date.updated	2024-08-07T05:15:30Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Nowadays, capturing cherished moments results in an abundance of photos, which necessitates the selection of the finest one from a pool of akin images—a process both intricate and time-intensive. Thus, series photo selection (SPS) techniques have been developed to recommend the optimal moment from nearly identical photos through the use of aesthetic quality assessment. However, addressing SPS proves demanding due to the subtle nuances within such imagery. Existing approaches predominantly rely on diverse feature types (e.g., color, layout, generic features) extracted from original images to discern the qualified shot, yet they disregard disentangling generality and specificity at the feature level. This study aims to detect subtle aesthetic distinctions among akin photos. We propose a feature separation model that captures all label-relevant information through an encoder. We introduce Information Bottleneck (IB) learning to obtain non-redundant representations of image pairs and filter out noise information from the representations. Our model segregates image features into shared and specific attributes by employing feature constraints to boost mutual information across images and guide meaningful information within individual images. This process filters out extraneous data within individual images, thus significantly enhancing the representation of similar image pairs. Extensive experiments on the Phototriage dataset show that our model can accentuate subtle disparities and achieve superior results when compared to alternative methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180289