article

Rapid and brief communication: Active learning for image retrieval with Co-SVM

Authors:

Jian Cheng,

Kongqiao WangAuthors Info & Claims

Pattern Recognition, Volume 40, Issue 1

Pages 330 - 334

https://doi.org/10.1016/j.patcog.2006.06.005

Published: 01 January 2007 Publication History

Abstract

In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning algorithm, Co-SVM, to improve the performance of selective sampling in image retrieval. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifiers are learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples which are differently classified by the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval.

References

[1]

Rui, Y., Huang, T.S. and Chang, S.F., Image retrieval: current techniques, promising directions and open issues. J. Visual Commun. Image Representation. v10. 39-62.

Digital Library

Google Scholar

[2]

S. Tong, E. Chang, Support vector machine active learning for image retrieval, in: Proceedings of the Ninth ACM International Conference on Multimedia, 2001, pp. 107-118.

Digital Library

Google Scholar

[3]

A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the 11th Annual Conference on Computational Learning Theory, 1998, pp. 92-100.

Digital Library

Google Scholar

[4]

I. Muslea, S. Minton, C.A. Knoblock, Selective sampling with redundant views, in: Proceedings of the 17th National Conference on Artificial Intelligence, 2000, pp. 621-626.

Digital Library

Google Scholar

[5]

Vapnik, V., Statistical Learning Theory. Wiley, New York.

Google Scholar

Cited By

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Lin SGao JZhang SHe XSheng YChen J(2020)A continuous learning method for recognizing named entities by integrating domain contextual relevance measurement and Web farming mode of Web intelligenceWorld Wide Web10.1007/s11280-019-00758-x23:3(1769-1790)Online publication date: 15-Feb-2020
https://dl.acm.org/doi/10.1007/s11280-019-00758-x
Lu WDai HZhang ZWu CZhuang Y(2018)Active instance matching with pairwise constraints and its application to Chinese knowledge base constructionKnowledge and Information Systems10.1007/s10115-017-1076-755:1(171-214)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1076-7
Zhang XYang YFeng J(2018)ML-LocNet: Improving Object Localization with Multi-view Learning NetworkComputer Vision – ECCV 201810.1007/978-3-030-01219-9_15(248-263)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01219-9_15
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Index Terms

Rapid and brief communication: Active learning for image retrieval with Co-SVM
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
      2. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

Pattern Recognition Volume 40, Issue 1

January, 2007

350 pages

ISSN:0031-3203

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Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2007

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Lin SGao JZhang SHe XSheng YChen J(2020)A continuous learning method for recognizing named entities by integrating domain contextual relevance measurement and Web farming mode of Web intelligenceWorld Wide Web10.1007/s11280-019-00758-x23:3(1769-1790)Online publication date: 15-Feb-2020
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Lu WDai HZhang ZWu CZhuang Y(2018)Active instance matching with pairwise constraints and its application to Chinese knowledge base constructionKnowledge and Information Systems10.1007/s10115-017-1076-755:1(171-214)Online publication date: 1-Apr-2018
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Selective sampling based on dynamic certainty propagation for image retrieval

Asymmetric propagation based batch mode active learning for image retrieval

Support vector machine active learning with applications to text classification

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