A ten-year literature review of content-based image retrieval (CBIR) studies in the tourism industry

Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less la...

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Veröffentlicht in:Electronic library Jg. 39; H. 2; S. 225 - 238
Hauptverfasser: Ammatmanee, Chanattra, Gan, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Emerald Publishing Limited 17.07.2021
Emerald Group Publishing Limited
Schlagworte:
Authorship
Co authorship
Comparative analysis
Digital imaging
Errors
Experiments
Extraction
Feature extraction
Human error
Image management
Image retrieval
Indexing
Labor costs
Literary criticism
Literature reviews
Neural networks
Research methodology
Retrieval
Retrieval performance measures
Semantics
Smartphones
Social networks
Tourism
Trends
Visual perception
United States > US
Tourism
Content-based image retrieval
Digital image indexing
Tourism industry
ISSN:0264-0473, 1758-616X
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Abstract Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions. Design/methodology/approach A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments. Findings Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored. Originality/value The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.
AbstractList PurposeDue to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions.Design/methodology/approachA systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments.FindingsBased on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored.Originality/valueThe fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.
Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions. Design/methodology/approach A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments. Findings Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored. Originality/value The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.
Author Gan, Lu
Ammatmanee, Chanattra
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Cites_doi 10.1108/EL-10-2016-0219
10.1109/TCYB.2014.2383389
10.1109/PIMRC.2010.5671757
10.1007/s11042-014-2191-z
10.1016/S0261-5177(99)00080-1
10.1007/s11042-015-3167-3
10.1109/TIP.2014.2330763
10.1007/s10462-019-09715-6
10.1109/TIP.2017.2655449
10.1007/s11042-016-3788-1
10.1016/0160-7383(94)90121-X
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Issue 2
Keywords Tourism
Content-based image retrieval
Digital image indexing
Tourism industry
Language English
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Snippet Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have...
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SubjectTerms Authorship
Co authorship
Comparative analysis
Digital imaging
Errors
Experiments
Extraction
Feature extraction
Human error
Image management
Image retrieval
Indexing
Labor costs
Literary criticism
Literature reviews
Neural networks
Research methodology
Retrieval
Retrieval performance measures
Semantics
Smartphones
Social networks
Tourism
Trends
Visual perception
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