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Feature synthesized EM algorithm for image retrieval

Published:16 May 2008Publication History
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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 labeled 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.

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 4, Issue 2
        May 2008
        197 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/1352012
        Issue’s Table of Contents

        Copyright © 2008 ACM

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        Publication History

        • Published: 16 May 2008
        • Accepted: 1 July 2007
        • Revised: 1 October 2006
        • Received: 1 June 2006
        Published in tomm Volume 4, Issue 2

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