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Metadata in video databases

Authors:

Arun HampapurAuthors Info & Claims

ACM SIGMOD Record, Volume 23, Issue 4

Pages 27 - 33

https://doi.org/10.1145/190627.190638

Published: 01 December 1994 Publication History

Abstract

Video is composed of audio-visual information. Providing content based access to video data is essential for the sucessful integration of video into computers. Organizing video for content based access requires the use of video metadata. This paper explores the nature video metadata. A data model for video databases is presented based on a study of the applications of video, the nature of video retrieval requests, and the features of video. The data model is used in the architectural framework of a video database. The current state of technology in video databases is summarized and research issues are highlighted.

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Index Terms

Metadata in video databases
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Information systems
  1. Data management systems

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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 23, Issue 4

Dec. 1994

98 pages

ISSN:0163-5808

DOI:10.1145/190627

Editor:
Arie Segev
The Univ. of California and Lawrence Berkeley Laboratory

Issue’s Table of Contents

Copyright © 1994 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1994

Published in SIGMOD Volume 23, Issue 4

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Cited By

Kang DBailis PZaharia M(2019)BlazeItProceedings of the VLDB Endowment10.14778/3372716.337272513:4(533-546)Online publication date: 9-Dec-2019
https://dl.acm.org/doi/10.14778/3372716.3372725
Bishop S(2018)Vlogging Parlance: Strategic Talking in Beauty VlogsMicrocelebrity Around the Globe10.1108/978-1-78756-749-820181002(21-32)Online publication date: 23-Oct-2018
https://doi.org/10.1108/978-1-78756-749-820181002
Kang DEmmons JAbuzaid FBailis PZaharia M(2017)NoScopeProceedings of the VLDB Endowment10.14778/3137628.313766410:11(1586-1597)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.14778/3137628.3137664
Patel SDeodeshmukh V(2017)Effective Video Retrieval System using Adaptive Shot detection and feature vector algorithm2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA)10.1109/ICCUBEA.2017.8463898(1-3)Online publication date: Aug-2017
https://doi.org/10.1109/ICCUBEA.2017.8463898
Aved ABlasch E(2016)Context Understanding from Query-Based Streaming VideoContext-Enhanced Information Fusion10.1007/978-3-319-28971-7_19(507-537)Online publication date: 26-May-2016
https://doi.org/10.1007/978-3-319-28971-7_19
Aved ABlasch E(2015)Multi-INT Query Language for DDDAS DesignsProcedia Computer Science10.1016/j.procs.2015.05.36051:C(2518-2532)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.360
Blasch EAved A(2015)Dynamic Data-driven Application System (DDDAS) for Video Surveillance User SupportProcedia Computer Science10.1016/j.procs.2015.05.35951:C(2503-2517)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.359
Zigomitros APatsakis C(2013)Storing Metadata as QR Codes in Multimedia StreamsMetadata and Semantics Research10.1007/978-3-319-03437-9_16(152-162)Online publication date: 2013
https://doi.org/10.1007/978-3-319-03437-9_16
Kumar CVali M(2013)Political Empowerment of Women in Panchayati Raj Institutions at Grassroots Level: A Case Study in Guntur District of Andhra PradeshPoverty & Public Policy10.1002/pop4.104:3(153-179)Online publication date: 25-Jan-2013
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Motoi SMisu TNakada YYazaki TKobayashi GMatsumoto TYagi N(2012)Bayesian event detection for sport games with hidden Markov modelPattern Analysis & Applications10.5555/2736786.273702915:1(59-72)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.5555/2736786.2737029
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