Feature Synthesized EM Algorithm for Image Retrieval

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@Article{Li:2008:TOMM,
  author =       "Rui Li and Bir Bhanu and Anlei Dong",
  title =        "Feature Synthesized EM Algorithm for Image Retrieval",
  journal =      "ACM Transactions on Multimedia Computing,
                 Communications, and Applications",
  year =         "2008",
  volume =       "4",
  number =       "2",
  pages =        "10:1--10:24",
  month =        may,
  keywords =     "genetic algorithms, genetic programming,
                 Coevolutionary feature synthesis, content-based image
                 retrieval, expectation maximization, semi-supervised
                 learning",
  ISSN =         "1551-6857",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.3189",
  URL =          "http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Journals/2008/Feature%20synthesized%20EM%20algorithm%20for%20image%20retrieval08.pdf",
  URL =          "http://doi.acm.org/10.1145/1352012.1352014",
  DOI =          "doi:10.1145/1352012.1352014",
  acmid =        "1352014",
  publisher =    "ACM",
  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 Synthesised
                 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 synthesized 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 =        "Replaces \cite{Li:2005:MULTIMEDIA} Also known as
                 \cite{Li:2008:FSE:1352012.1352014}",
}

Genetic Programming entries for Rui Li Bir Bhanu Anlei Dong

Citations