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