research-article

High scale video mining with forests of fuzzy decision trees

Authors:

Christophe Marsala,

Marcin DetynieckiAuthors Info & Claims

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

Pages 413 - 418

https://doi.org/10.1145/1456223.1456308

Published: 28 October 2008 Publication History

Abstract

In this paper, a video mining method based on the use of Forests of Fuzzy Decision Trees (FFDT) is presented. We focus on the use of such a FFDT in a high scale video mining application and highlight the main advantages of using fuzzy set theory in such a process.

References

[1]

Guidelines for the TRECVID 2006 evaluation - National Institute of Standards and Technology, 2006. http://www-nlpir.nist.gov/projects/tv2006/tv2006.html.

[2]

A. Asuncion and D. Newman. UCI machine learning repository - University of California, Irvine, School of Information and Computer Sciences. http://www.ics.uci.edu/~mlearn/MLRepository.html, 2007.

[3]

P. P. Bonissone, J. M. Cadenas, M. C. Garrido, and R. A. Díaz-Valladares. A fuzzy random forest: Fundamental for design and construction. In Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'08), pages 1231--1238, Malaga, Spain, July 2008.

[4]

L. Breiman. Random forests. Machine Learning, 45:5--32, 2001.

Digital Library

[5]

L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification And Regression Trees. Chapman and Hall, New York, 1984.

[6]

K. Crockett, Z. Bandar, and D. Mclean. Growing a fuzzy decision forest. In Proceedings of the 10th IEEE International Conference on Fuzzy Systems, pages 614--617, December 2001.

[7]

P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine Learning, 63(1):3--42, April 2006.

Digital Library

[8]

C. Z. Janikow and M. Faifer. Fuzzy decision forest. In Proceedings of the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'00), pages 218--221, July 2000.

[9]

C. Marsala and B. Bouchon-Meunier. Forest of fuzzy decision trees. In M. Mareš, R. Mesiar, V. Novák, J. Ramik, and A. Stupňanová, editors, Proceedings of the Seventh International Fuzzy Systems Association World Congress, volume 1, pages 369--374, Prague, Czech Republic, June 1997.

[10]

C. Marsala and B. Bouchon-Meunier. An adaptable system to construct fuzzy decision trees. In Proc. of the NAFIPS'99 (North American Fuzzy Information Processing Society), pages 223--227, New York, USA, 1999.

[11]

C. Marsala and M. Detyniecki. University of Paris 6 at TRECVID 2005: High-level feature extraction. In TREC Video Retrieval Evaluation Online Proceedings, 2005. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html.

[12]

C. Marsala and M. Detyniecki. University of Paris 6 at TRECVID 2006: Forests of fuzzy decision trees for high-level feature extraction. In TREC Video Retrieval Evaluation Online Proceedings, November 2006. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html.

[13]

C. Marsala, M. Detyniecki, N. Usunier, and M.-R. Amini. High-level feature detection with forests of fuzzy decision trees combined with the rankboost algorithm. In TREC Video Retrieval Evaluation Online Proceedings, 2007. http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html.

[14]

P. Over, W. Kraaij, and A. F. Smeaton. Guidelines for the TRECVID 2007 evaluation - National Institute of Standards and Technology, 2007. http://www-nlpir.nist.gov/projects/tv2007/tv2007.html.

[15]

C. Petersohn. Fraunhofer HHI at TRECVID 2004: Shot boundary detection system. Technical report, TREC Video Retrieval Evaluation Online Proceedings, TRECVID, 2004. http://www-nlpir.nist.gov/projects/tvpubs/tvpapers04/fraunhofer.pdf.

Cited By

Gu YZydek DJin Z(2015)Active Learning based on Random Forest and Its Application to Terrain ClassificationProgress in Systems Engineering10.1007/978-3-319-08422-0_41(273-278)Online publication date: 2015
https://doi.org/10.1007/978-3-319-08422-0_41
Yu GYuan JLiu Z(2013)Action Search by Example Using Randomized Visual VocabulariesIEEE Transactions on Image Processing10.1109/TIP.2012.221627322:1(377-390)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1109/TIP.2012.2216273
Gang Yu Junsong Yuan Zicheng Liu (2011)Unsupervised random forest indexing for fast action searchProceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2011.5995488(865-872)Online publication date: 20-Jun-2011
https://dl.acm.org/doi/10.1109/CVPR.2011.5995488

Index Terms

High scale video mining with forests of fuzzy decision trees
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
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

cover image ACM Other conferences

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

October 2008

733 pages

ISBN:9781605580463

DOI:10.1145/1456223

Conference Chair:
Lotfi A. Zadeh
University of California
,
General Chairs:
Youakim Badr
INSA-Lyon, France
,
Ajith Abraham
Norwegian University of Science and Technology, Norway
,
Yukio Ohsawa
Tokyo Institute of Technology, Japan
,
Program Chairs:
Richard Chbeir
University of Bourgogne, France
,
Fernando Ferri
IRPPS-CNR, Italy
,
Mario Koeppen
Kyushu Institute of Technology, Japan
,
Dominique Laurent
University of Cergy-Pontoise, France

Copyright © 2008 ACM.

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]

Sponsors

The French Chapter of ACM Special Interest Group on Applied Computing
Ministère des Affaires Etrangères et Européennes
Région Ile de France
Communauté d'Agglomération de Cergy-Pontoise
Institute of Electrical and Electronics Engineers Systems, Man and Cybernetics Society
The European Society For Fuzzy And technology
Institute of Electrical and Electronics Engineers France Section
Laboratoire des Equipes Traitement des Images et du Signal
AFIHM: Ass. Francophone d'Interaction Homme-Machine
The International Fuzzy System Association
Laboratoire Innovation Développement
University of Cergy-Pontoise
The World Federation of Soft Computing
Agence de Développement Economique de Cergy-Pontoise
The European Neural Network Society
Comité d'Expansion Economique du Val d'Oise

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2008

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Cited By

Gu YZydek DJin Z(2015)Active Learning based on Random Forest and Its Application to Terrain ClassificationProgress in Systems Engineering10.1007/978-3-319-08422-0_41(273-278)Online publication date: 2015
https://doi.org/10.1007/978-3-319-08422-0_41
Yu GYuan JLiu Z(2013)Action Search by Example Using Randomized Visual VocabulariesIEEE Transactions on Image Processing10.1109/TIP.2012.221627322:1(377-390)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1109/TIP.2012.2216273
Gang Yu Junsong Yuan Zicheng Liu (2011)Unsupervised random forest indexing for fast action searchProceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2011.5995488(865-872)Online publication date: 20-Jun-2011
https://dl.acm.org/doi/10.1109/CVPR.2011.5995488

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