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Coevolutionary feature synthesized EM algorithm for image retrieval (2005)
| Venue: | In Proceedings of the ACM Multimedia Conference (ACMMM), Singapore, ACM |
| Citations: | 1 - 0 self |
BibTeX
@INPROCEEDINGS{Li05coevolutionaryfeature,
author = {Rui Li and Bir Bhanu and Anlei Dong},
title = {Coevolutionary feature synthesized EM algorithm for image retrieval},
booktitle = {In Proceedings of the ACM Multimedia Conference (ACMMM), Singapore, ACM},
year = {2005},
pages = {696--705}
}
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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.
Keyphrases
image retrieval em algorithm coevolutionary feature real image synthesized low-dimensional feature space local maximum coevolutionary feature synthesized expectation-maximization coevolutionary genetic programming classification performance content-based image retrieval original high-dimensional space query phase classification computation several limitation kernel-based method
