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Construction of a hierarchical classifier schema using a combination of text-based and image-based approaches

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SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 438 - 439

https://doi.org/10.1145/383952.384075

Published: 01 September 2001 Publication History

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Abstract

Web document hierarchical classification approaches often rely on textual features alone even though web pages include multimedia data. We propose a new hierarchical integrated web classification approach that combines image-based and text-based approaches. Instead of using a flat classifier to combine text and image classification, we perform classification on a hierarchy differently on different levels of the tree, using text for branches and images only at leaves. The results of our experiments show that the use of the hierarchical structure improved web document classification performance significantly.

References

[1]

S. Chakrabarti, B. Dom, R. Agrawal, and P. Raghavan, Using taxonomy, discriminants, and signatures for navigating in text databases. In Proceedings of the 23 rd VLDB Conference, Athens, 1997.

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Google Scholar

[2]

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1997.

Digital Library

Google Scholar

[3]

S. Paek, C. L. Sable, V. Hatzivassiloglou, A. Jaimes, B. H. Schiffman, S.-F. Chang, and K. R. McKeown, Integration of visual and text based approaches for the content labeling and classification of Photographs, ACM SIGIR'99 Workshop on Multimedia Indexing and Retrieval, Berkeley, CA, Aug. 19, 1999.

Google Scholar

[4]

M.S. Drew, J. Wei, and Z.-N.Li, Illumination-Invariant Color Object Recognition via Compressed Chromaticity Histograms of Normalized Images, Int. Conf. on Comp. Vision, Bombay, pp. 533-540, 1998.

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Cited By

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Jiang WRas Z(2013)Multi-label automatic indexing of music by cascade classifiersWeb Intelligence and Agent Systems10.5555/2590084.259008811:2(149-170)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.5555/2590084.2590088
Kalva PEnembreck FKoerich A(2008)WEB Image Classification using Classifier CombinationIEEE Latin America Transactions10.1109/TLA.2008.49174396:7(661-671)Online publication date: Dec-2008
https://doi.org/10.1109/TLA.2008.4917439
Kalva PEnembreck FKoerich A(2007)WEB Image Classification Based on the Fusion of Image and Text ClassifiersNinth International Conference on Document Analysis and Recognition (ICDAR 2007)10.1109/ICDAR.2007.4378772(561-568)Online publication date: Sep-2007
https://doi.org/10.1109/ICDAR.2007.4378772
Show More Cited By

Index Terms

Construction of a hierarchical classifier schema using a combination of text-based and image-based approaches
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Published In

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

September 2001

454 pages

ISBN:1581133316

DOI:10.1145/383952

Chairmen:
Donald H. Kraft
Louisiana State Univ.
,
W. Bruce Croft
University of Massachusetts, (For the Americas)
,
David J. Harper
The Robert Gordon University, (For Europe and Africa)
,
Justin Zobel
RMIT University, (For Asia and Australasia)

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: 01 September 2001

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SIGIR

SIGIR01: 24th ACM/SIGIR International Conference on Research and Development in Information Retrieval

Louisiana, New Orleans, USA

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SIGIR '01 Paper Acceptance Rate 47 of 201 submissions, 23%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Jiang WRas Z(2013)Multi-label automatic indexing of music by cascade classifiersWeb Intelligence and Agent Systems10.5555/2590084.259008811:2(149-170)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.5555/2590084.2590088
Kalva PEnembreck FKoerich A(2008)WEB Image Classification using Classifier CombinationIEEE Latin America Transactions10.1109/TLA.2008.49174396:7(661-671)Online publication date: Dec-2008
https://doi.org/10.1109/TLA.2008.4917439
Kalva PEnembreck FKoerich A(2007)WEB Image Classification Based on the Fusion of Image and Text ClassifiersNinth International Conference on Document Analysis and Recognition (ICDAR 2007)10.1109/ICDAR.2007.4378772(561-568)Online publication date: Sep-2007
https://doi.org/10.1109/ICDAR.2007.4378772
Shou-Bin Dong Yi-Ming Yang (2002)Hierarchical web image classification by multi-level featuresProceedings. International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2002.1174419(663-668)Online publication date: 2002
https://doi.org/10.1109/ICMLC.2002.1174419

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