research-article

Knowledge-Driven Video Information Retrieval with LOD: From Semi-Structured to Structured Video Metadata

Authors:

Leslie F. Sikos,

David M.W. PowersAuthors Info & Claims

ESAIR '15: Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval

Pages 35 - 37

https://doi.org/10.1145/2810133.2810141

Published: 22 October 2015 Publication History

Abstract

In parallel with the tremendously increasing number of video contents on the Web, many technical specifications and standards have been introduced to store technical details and describe the content of, and add subtitles to, online videos. Some of these specifications are based on unstructured data with limited machine-processability, data reuse, and interoperability, while others are XML-based, representing semi-structured data. While low-level video features can be derived automatically, high-level features are mainly related to a particular knowledge domain and heavily rely on human experience, judgment, and background. One of the approaches to solve this problem is to map standard, often semi-structured, vocabularies, such as that of MPEG-7, to machine-interpretable ontologies. Another approach is to introduce new multimedia ontologies. While video contents can be annotated efficiently with terms defined by structured LOD datasets, such as DBpedia, ontology standardization would be desired in the video production and distribution domains. This paper compares the state-of-the-art video annotations in terms of descriptor level and machine-readability, highlights the limitations of the different approaches, and makes suggestions towards standard video annotations.

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Cited By

Fourati MJedidi AGargouri F(2022)Semiotic and Thematic Process for Audiovisual Documents DescriptionInternational Journal of Intelligent Information Technologies10.4018/IJIIT.29623918:1(1-24)Online publication date: Jan-2022
https://doi.org/10.4018/IJIIT.296239
Patel AVyas RVyas OOjha M(2022)A study on video semantics; overview, challenges, and applicationsMultimedia Tools and Applications10.1007/s11042-021-11722-1Online publication date: 19-Jan-2022
https://doi.org/10.1007/s11042-021-11722-1
Patel AMerlino GBruneo DPuliafito AVyas OOjha M(2021)Video representation and suspicious event detection using semantic technologiesSemantic Web10.3233/SW-20039312:3(467-491)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-200393
Show More Cited By

Index Terms

Knowledge-Driven Video Information Retrieval with LOD: From Semi-Structured to Structured Video Metadata
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Dictionaries
    2. Search engine architectures and scalability
      1. Search engine indexing

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Information & Contributors

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Published In

cover image ACM Conferences

ESAIR '15: Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval

October 2015

62 pages

ISBN:9781450337908

DOI:10.1145/2810133

General Chairs:
Krisztian Balog
University of Stavanger, Norway
,
Jeffrey Dalton
Google Research, USA
,
Antoine Doucet
University of La Rochelle, L3i Laboratory, France
,
Yusra Ibrahim
Max Planck Institute for Informatics, Germany

Copyright © 2015 ACM.

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Publication History

Published: 22 October 2015

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 23, 2015

Melbourne, Australia

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ESAIR '15 Paper Acceptance Rate 10 of 19 submissions, 53%;

Overall Acceptance Rate 35 of 55 submissions, 64%

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Cited By

Fourati MJedidi AGargouri F(2022)Semiotic and Thematic Process for Audiovisual Documents DescriptionInternational Journal of Intelligent Information Technologies10.4018/IJIIT.29623918:1(1-24)Online publication date: Jan-2022
https://doi.org/10.4018/IJIIT.296239
Patel AVyas RVyas OOjha M(2022)A study on video semantics; overview, challenges, and applicationsMultimedia Tools and Applications10.1007/s11042-021-11722-1Online publication date: 19-Jan-2022
https://doi.org/10.1007/s11042-021-11722-1
Patel AMerlino GBruneo DPuliafito AVyas OOjha M(2021)Video representation and suspicious event detection using semantic technologiesSemantic Web10.3233/SW-20039312:3(467-491)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-200393
Fourati MJedidi AGargouri F(2020)A survey on description and modeling of audiovisual documentsMultimedia Tools and Applications10.1007/s11042-020-09589-9Online publication date: 15-Aug-2020
https://doi.org/10.1007/s11042-020-09589-9
Moreno MSantos WSantos RRamos ICerqueira R(2019)Supporting Soccer Analytics through HyperKnowledge Specifications2019 Second International Conference on Artificial Intelligence for Industries (AI4I)10.1109/AI4I46381.2019.00012(13-16)Online publication date: Sep-2019
https://doi.org/10.1109/AI4I46381.2019.00012
Sánchez-Nielsen EChávez-Gutiérrez FLorenzo-Navarro J(2019)A semantic parliamentary multimedia approach for retrieval of video clips with content understandingMultimedia Systems10.1007/s00530-019-00610-225:4(337-354)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00530-019-00610-2
Meditskos GVrochidis SKompatsiaris I(2019)V4Ann: Representation and Interlinking of Atom-Based Annotations of Digital ContentSemantic Systems. The Power of AI and Knowledge Graphs10.1007/978-3-030-33220-4_10(124-139)Online publication date: 4-Nov-2019
https://doi.org/10.1007/978-3-030-33220-4_10
Fayyaz NUllah IKhusro S(2018)On the Current State of Linked Open DataInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201810010614:4(110-128)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.4018/IJSWIS.2018100106
Sikos L(2018)VidOnt: a core reference ontology for reasoning over video scenesJournal of Information and Telecommunication10.1080/24751839.2018.14376962:2(192-204)Online publication date: 21-Feb-2018
https://doi.org/10.1080/24751839.2018.1437696
Sikos L(2018)Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal ReasoningBridging the Semantic Gap in Image and Video Analysis10.1007/978-3-319-73891-8_6(97-122)Online publication date: 21-Feb-2018
https://doi.org/10.1007/978-3-319-73891-8_6
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