Coevolutionary feature synthesized EM algorithm for image retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Li:2005:MULTIMEDIA,
  author =       "Rui Li and Bir Bhanu and Anlei Dong",
  title =        "Coevolutionary feature synthesized {EM} algorithm for
                 image retrieval",
  booktitle =    "Proceedings of the 13th Annual ACM International
                 Conference on Multimedia, MULTIMEDIA '05",
  year =         "2005",
  pages =        "696--705",
  address =      "Singapore",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming,
                 coevolutionary feature synthesis, content-based image
                 retrieval, expectation maximization algorithm,
                 semi-supervised learning",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.654.3189",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  isbn13 =       "1-59593-044-2",
  acmid =        "1101304",
  URL =          "http://doi.acm.org/10.1145/1101149.1101304",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.3189",
  DOI =          "doi:10.1145/1101149.1101304",
  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.",
  notes =        "See \cite{Li:2008:TOMM}",
}

Genetic Programming entries for Rui Li Bir Bhanu Anlei Dong

Citations