Article

An online-optimized incremental learning framework for video semantic classification

Authors:

Jun Wu,

Xian-Sheng Hua,

Hong-Jiang Zhang,

Bo ZhangAuthors Info & Claims

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Pages 320 - 323

https://doi.org/10.1145/1027527.1027601

Published: 10 October 2004 Publication History

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Abstract

This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic classification framework, termed OOIL (for Online-Optimized Incremental Learning), in which two sets of optimized classification models, local and global, are online trained by sufficiently exploiting both local and global statistic characteristics of videos. The global models are pre-trained on a relatively small set of pre-labeled samples. And the local models are optimized for the under-test video or video segment by checking a small portion of unlabeled samples in this video, while they are also applied to incrementally update the global models. Experiments have illustrated promising results on simulated data as well as real sports videos.

References

[1]

P. Xu, et al, Algorithms and Systems for Segmentation and Structure Analysis in Soccer Video, ICME2001, pp 22--25.]]

Google Scholar

[2]

L. Xie, et al, Structure Analysis of Soccer Video with Hidden Markov Models, ICASSP 2002, Vol 4, pp 13--17.]]

Google Scholar

[3]

Y.Gong, T.S. Lim, and H.C. Chua, Automatic Parsing of TV Soccer Programs, ICMCS 1995, pp 167--174.]]

Digital Library

Google Scholar

[4]

D. Zhong, S-F. Chang, Structure Analysis of Sports Video Using Domain Models, ICME 2001, pp 713--716.]]

Google Scholar

[5]

G. Sudihir, J.C.M. Lee, A.K. Jain, Automatic classification of tennis video for high-level content-based retrieval, IEEE Intl Workshop on CBAIVD 1998.]]

Digital Library

Google Scholar

[6]

Baoxin Li, et al, Event Detection and Summarization in Sports Video, IEEE Intl Workshop on CBAIVL 2001.]]

Digital Library

Google Scholar

[7]

S. Raaijmakers, et al, Multimodal topic segmentation and classification of news video, ICME 2002, Vol 2, pp 33--36.]]

Crossref

Google Scholar

[8]

M. Figueiredo, et al, On Fitting Mixture Models, Energy Minimization in Computer Vision and Pattern Recognition, E. Hancock and M. Pellilo (Eds.), Springer-Verlag, 1999.]]

Digital Library

Google Scholar

[9]

S. Kullback, Information Theory and Statistics, J. Wiley & Sons, New York, 1959.]]

Google Scholar

[10]

M. West, J. Harrison, Bayesian Forecasting and Dynamic Models, Springer Verlag, New York, 1989.]]

Digital Library

Google Scholar

[11]

Jianping Fan, et al, Semantic video classification by integrating flexible mixture model with adaptive EM algorithm, ACM SIGMM MIR 2003, pp 9--16.]]

Digital Library

Google Scholar

Cited By

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Guo JCheng YWang DTao FPickl S(2021)Industrial Dataspace for smart manufacturing: connotation, key technologies, and frameworkInternational Journal of Production Research10.1080/00207543.2021.195599661:12(3868-3883)Online publication date: 16-Aug-2021
https://doi.org/10.1080/00207543.2021.1955996
Ou DJi YZhang LLiu H(2020)An Online Classification Method for Fault Diagnosis of Railway TurnoutsSensors10.3390/s2016462720:16(4627)Online publication date: 17-Aug-2020
https://doi.org/10.3390/s20164627
Sun FXu MLi HHao S(2016)Social video annotation by combining features with a tri-adaptation approachMultimedia Systems10.1007/s00530-014-0405-x22:4(413-422)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00530-014-0405-x
Show More Cited By

Index Terms

An online-optimized incremental learning framework for video semantic classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

October 2004

1028 pages

ISBN:1581138938

DOI:10.1145/1027527

General Chairs:
Henning Schulzrinne
Columbia University
,
Nevenka Dimitrova
Philips Research
,
Program Chairs:
Angela Sasse
UCL
,
Sue Moon
KAIST
,
Rainer Lienhart
U Augsburg

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

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Published: 10 October 2004

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MM04

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 10 - 16, 2004

NY, New York, USA

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

View all

Guo JCheng YWang DTao FPickl S(2021)Industrial Dataspace for smart manufacturing: connotation, key technologies, and frameworkInternational Journal of Production Research10.1080/00207543.2021.195599661:12(3868-3883)Online publication date: 16-Aug-2021
https://doi.org/10.1080/00207543.2021.1955996
Ou DJi YZhang LLiu H(2020)An Online Classification Method for Fault Diagnosis of Railway TurnoutsSensors10.3390/s2016462720:16(4627)Online publication date: 17-Aug-2020
https://doi.org/10.3390/s20164627
Sun FXu MLi HHao S(2016)Social video annotation by combining features with a tri-adaptation approachMultimedia Systems10.1007/s00530-014-0405-x22:4(413-422)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00530-014-0405-x
Khoshrou SCardoso JTeixeira L(2014)Active Learning from Video Streams in a Multi-camera ScenarioProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.224(1248-1253)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.224
Hu WXie NLi Zeng XMaybank S(2011)A Survey on Visual Content-Based Video Indexing and RetrievalIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.210971041:6(797-819)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1109/TSMCC.2011.2109710
Tang JHua XSong YMei TWu X(2008)Optimizing training set construction for video semantic classificationEURASIP Journal on Advances in Signal Processing10.1155/2008/6937312008(12)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1155/2008/693731
Jiangwei LYunhong W(2007)Video-based face tracking and recognition on updating twin GMMsProceedings of the 2007 international conference on Advances in Biometrics10.5555/2391659.2391756(848-857)Online publication date: 27-Aug-2007
https://dl.acm.org/doi/10.5555/2391659.2391756
Jiangwei LYunhong W(2007)Video-Based Face Tracking and Recognition on Updating Twin GMMsAdvances in Biometrics10.1007/978-3-540-74549-5_89(848-857)Online publication date: 2007
https://doi.org/10.1007/978-3-540-74549-5_89
Song YHua XQi GDai LWang MZhang HWang JBoujemaa NChen Y(2006)Efficient semantic annotation method for indexing large personal video databaseProceedings of the 8th ACM international workshop on Multimedia information retrieval10.1145/1178677.1178716(289-296)Online publication date: 26-Oct-2006
https://dl.acm.org/doi/10.1145/1178677.1178716
Meng Wang Xian-Sheng Hua Yan Song Li-Rong Dal ShiPeng Li (2006)Automatic video annotation based on co-adaptation and label correction2006 IEEE International Symposium on Circuits and Systems10.1109/ISCAS.2006.1693881(4)Online publication date: 2006
https://doi.org/10.1109/ISCAS.2006.1693881
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