Coevolutionary feature synthesized EM algorithm for image retrieval
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Li:2005:MULTIMEDIA,
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author = "Rui Li and Bir Bhanu and Anlei Dong",
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title = "Coevolutionary feature synthesized {EM} algorithm for
image retrieval",
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booktitle = "Proceedings of the 13th Annual ACM International
Conference on Multimedia, MULTIMEDIA '05",
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year = "2005",
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pages = "696--705",
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address = "Singapore",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming,
coevolutionary feature synthesis, content-based image
retrieval, expectation maximization algorithm,
semi-supervised learning",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.654.3189",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
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isbn13 = "1-59593-044-2",
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acmid = "1101304",
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URL = "http://doi.acm.org/10.1145/1101149.1101304",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.3189",
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DOI = "doi:10.1145/1101149.1101304",
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abstract = "As a commonly used unsupervised learning algorithm in
Content-Based Image Retrieval (CBIR),
Expectation-Maximization (EM) algorithm has several
limitations, including the curse of dimensionality and
the convergence at a local maximum. In this article, we
propose a novel learning approach, namely
Coevolutionary Feature Synthesized
Expectation-Maximization (CFS-EM), to address the above
problems. The CFS-EM is a hybrid of coevolutionary
genetic programming (CGP) and EM algorithm applied on
partially labelled data. CFS-EM is especially suitable
for image retrieval because the images can be searched
in the synthesised low-dimensional feature space, while
a kernel-based method has to make classification
computation in the original high-dimensional space.
Experiments on real image databases show that CFS-EM
outperforms Radial Basis Function Support Vector
Machine (RBF-SVM), CGP, Discriminant-EM (D-EM) and
Transductive-SVM (TSVM) in the sense of classification
performance and it is computationally more efficient
than RBF-SVM in the query phase.",
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notes = "See \cite{Li:2008:TOMM}",
- }
Genetic Programming entries for
Rui Li
Bir Bhanu
Anlei Dong
Citations