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

Authors:

Kien A. HuaAuthors Info & Claims

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

Pages 415 - 426

https://doi.org/10.1145/342009.335436

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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Efficient and cost-effective techniques for browsing and indexing large video databases
1. Information systems

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cover image ACM Conferences

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

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
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Copyright © 2000 ACM.

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Published: 16 May 2000

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SIGMOD/PODS00: ACM International Conference on Management of Data and Symposium on Principles of Database Systems

May 15 - 18, 2000

Texas, Dallas, USA

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SIGMOD '00 Paper Acceptance Rate 42 of 248 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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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
https://dl.acm.org/doi/10.1145/2695664.2695841
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
https://dl.acm.org/doi/10.1109/ISM.2014.52
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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Peng RAved AHua K(2012)Real-Time Query Processing on Live Videos in Networks of Distributed CamerasResearch, Practice, and Educational Advancements in Telecommunications and Networking10.4018/978-1-4666-0050-8.ch003(27-48)Online publication date: 2012
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Girgensohn AShipman FWilcox LDe Natale FDel Bimbo AHanjalic AManjunath BSatoh S(2011)Adaptive clustering and interactive visualizations to support the selection of video clipsProceedings of the 1st ACM International Conference on Multimedia Retrieval10.1145/1991996.1992030(1-8)Online publication date: 18-Apr-2011
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