Coevolutionary feature synthesized EM algorithm for image retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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