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Efficient and cost-effective techniques for browsing and indexing large video databases

Authors:

Kien A. HuaAuthors Info & Claims

ACM SIGMOD Record, Volume 29, Issue 2

Pages 415 - 426

https://doi.org/10.1145/335191.335436

Published: 16 May 2000 Publication History

Abstract

We present in this paper a fully automatic content-based approach to organizing and indexing video data. Our methodology involves three steps:

Step 1: We segment each video into shots using a Camera-Tracking technique. This process also extracts the feature vector for each shot, which consists of two statistical variances Var^BA and Var^OA. These values capture how much things are changing in the background and foreground areas of the video shot.

Step 2: For each video, We apply a fully automatic method to build a browsing hierarchy using the shots identified in Step 1.

Step 3: Using the Var^BA and Var^OA values obtained in Step 1, we build an index table to support a variance-based video similarity model. That is, video scenes/shots are retrieved based on given values of Var^BA and Var^OA.

The above three inter-related techniques offer an integrated framework for modeling, browsing, and searching large video databases. Our experimental results indicate that they have many advantages over existing methods.

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Peng RAved AHua K(2010)Real-Time Query Processing on Live Videos in Networks of Distributed CamerasInternational Journal of Interdisciplinary Telecommunications and Networking10.4018/jitn.20100101032:1(27-48)Online publication date: 1-Jan-2010
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Lo YLee CWang C(2009)Scalable multi-feature index structure for music databasesInformation Sciences: an International Journal10.1016/j.ins.2009.03.019179:15(2662-2675)Online publication date: 1-Jul-2009
https://dl.acm.org/doi/10.1016/j.ins.2009.03.019
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Efficient and cost-effective techniques for browsing and indexing large video databases
1. Information systems

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Efficient and cost-effective techniques for browsing and indexing large video databases
SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 29, Issue 2

June 2000

609 pages

ISSN:0163-5808

DOI:10.1145/335191

Editors:
Weidong Chen
Southern Methodist Univ., Dallas, TX
,
Jeffrey Naughton
Univ. of Wisconsin-Madison, Madison
,
Philip A. Bernstein
Microsoft

Issue’s Table of Contents

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data
May 2000
604 pages
ISBN:1581132174
DOI:10.1145/342009
Chairmen:
Maggie Dunham
Southern Methodist Univ.
,
Jeffrey F. Naughton
Univ. of Wisconsin-Madison
,
Weidong Chen
Southern Methodist Univ.
,
Nick Koudas
AT &T Labs

Copyright © 2000 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2000

Published in SIGMOD Volume 29, Issue 2

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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
Peng RAved AHua K(2010)Real-Time Query Processing on Live Videos in Networks of Distributed CamerasInternational Journal of Interdisciplinary Telecommunications and Networking10.4018/jitn.20100101032:1(27-48)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.4018/jitn.2010010103
Lo YLee CWang C(2009)Scalable multi-feature index structure for music databasesInformation Sciences: an International Journal10.1016/j.ins.2009.03.019179:15(2662-2675)Online publication date: 1-Jul-2009
https://dl.acm.org/doi/10.1016/j.ins.2009.03.019
Li Y(2009)VideoEncyclopedia of Database Systems10.1007/978-0-387-39940-9_1017(3265-3270)Online publication date: 2009
https://doi.org/10.1007/978-0-387-39940-9_1017
Aner-Wolf AKender J(2003)Beyond Key-Frames: The Physical Setting as a Video Mining PrimitiveVideo Mining10.1007/978-1-4757-6928-9_2(31-60)Online publication date: 2003
https://doi.org/10.1007/978-1-4757-6928-9_2
Lai ZHan CLiu CZhang PLo EKao BLi GLi ZIdreos SSrivastava D(2021)Top-K Deep Video AnalyticsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452786(1037-1050)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452786
Xu TBotelho LLin F(2019)VStoreProceedings of the Fourteenth EuroSys Conference 201910.1145/3302424.3303971(1-17)Online publication date: 25-Mar-2019
https://dl.acm.org/doi/10.1145/3302424.3303971
Barbieri TTrojahn TPonti-Jr MGoularte RWainwright RCorchado JBechini AHong J(2015)Shot-HRProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695841(1257-1262)Online publication date: 13-Apr-2015
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Barbieri TGoularte R(2014)KS-SIFTProceedings of the 2014 IEEE International Symposium on Multimedia10.1109/ISM.2014.52(13-17)Online publication date: 10-Dec-2014
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de Souza TGoularte RShin SMaldonado J(2013)Video shot representation based on histogramsProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480547(961-966)Online publication date: 18-Mar-2013
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