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Detecting events by clustering videos from large media databases

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Ari VisaAuthors Info & Claims

EiMM '10: Proceedings of the 2nd ACM international workshop on Events in multimedia

Pages 9 - 14

https://doi.org/10.1145/1877937.1877942

Published: 25 October 2010 Publication History

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Abstract

As the number of user-generated videos rises in the Internet, there is a growing need for more efficient search tools that enable the users to find the desired content. Moreover, the associated video metadata for the content is often incomplete or even misleading.

This paper addresses the problem of finding events by utilizing the video metadata from a video database by proposing two novel methods that are used in parallel. The first one is missing data compensation, which harvests missing data values from the textual descriptions in the video metadata. The second one is a layered clustering method that divides the videos in the database into clusters, each of which is considered as an event.

The methods are tested with manually selected data from YouTube. The results show that missing data compensation yields better results in terms of accuracy than using ram data, and that the clustering method provides acceptable results and is a promising approach for further research.

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Cited By

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Tong WYang YJiang LYu SLan ZMa ZSze WYounessian EHauptmann A(2014)E-LAMPMachine Vision and Applications10.1007/s00138-013-0529-625:1(5-15)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1007/s00138-013-0529-6
Kamie MHashimoto TKitagawa HOssowski SLecca P(2012)Effective web video clustering using playlist informationProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245460(949-956)Online publication date: 26-Mar-2012
https://dl.acm.org/doi/10.1145/2245276.2245460

Index Terms

Detecting events by clustering videos from large media databases
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering
    2. Multimedia information systems
      1. Multimedia databases

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Information & Contributors

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Published In

EiMM '10: Proceedings of the 2nd ACM international workshop on Events in multimedia

October 2010

68 pages

ISBN:9781450301763

DOI:10.1145/1877937

General Chairs:
Ansgar Scherp
University of Koblenz, Germany
,
Ramesh Jain
University of California at Irvine, CA, USA
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Vasileios Mezaris
Centre for Research and Technology Hellas, Greece

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25, 2010

Firenze, Italy

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EiMM '10 Paper Acceptance Rate 9 of 16 submissions, 56%;

Overall Acceptance Rate 19 of 36 submissions, 53%

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Cited By

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Tong WYang YJiang LYu SLan ZMa ZSze WYounessian EHauptmann A(2014)E-LAMPMachine Vision and Applications10.1007/s00138-013-0529-625:1(5-15)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1007/s00138-013-0529-6
Kamie MHashimoto TKitagawa HOssowski SLecca P(2012)Effective web video clustering using playlist informationProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245460(949-956)Online publication date: 26-Mar-2012
https://dl.acm.org/doi/10.1145/2245276.2245460

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