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Coevolutionary feature synthesized EM algorithm for image retrieval

Published: 06 November 2005 Publication History

Abstract

As a commonly used unsupervised learning algorithm in Content-Based Image Retrieval (CBIR), Expectation-Maximization (EM) algorithm has several limitations, especially in high dimensional feature spaces where the data are limited and the computational cost varies exponentially with the number of feature dimensions. Moreover, the convergence is guaranteed only at a local maximum. In this paper, we propose a unified framework of a novel learning approach, namely Coevolutionary Feature Synthesized Expectation-Maximization (CFS-EM), to achieve satisfactory learning in spite of these difficulties. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm. The advantages of CFS-EM are: 1) it synthesizes low-dimensional features based on CGP algorithm, which yields near optimal nonlinear transformation and classification precision comparable to kernel methods such as the support vector machine (SVM); 2) the explicitness of feature transformation is especially suitable for image retrieval because the images can be searched in the synthesized low-dimensional space, while kernel-based methods have to make classification computation in the original high-dimensional space; 3) the unlabeled data can be boosted with the help of the class distribution learning using CGP feature synthesis approach. Experimental results show that CFS-EM outperforms pure EM and CGP alone, and is comparable to SVM in the sense of classification. It is computationally more efficient than SVM in query phase. Moreover, it has a high likelihood that it will jump out of a local maximum to provide near optimal results and a better estimation of parameters.

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cover image ACM Conferences
MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
November 2005
1110 pages
ISBN:1595930442
DOI:10.1145/1101149
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2005

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Author Tags

  1. coevolutionary feature synthesis
  2. content-based image retrieval
  3. expectation maximization algorithm
  4. genetic programming
  5. semi-supervised learning

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2018)Feature synthesis for image classification and retrieval via one-against-all perceptronsNeural Computing and Applications10.1007/s00521-016-2504-429:4(943-957)Online publication date: 1-Feb-2018
  • (2016)A software system for automated identification and retrieval of moth images based on wing attributesPattern Recognition10.1016/j.patcog.2015.09.01251:C(225-241)Online publication date: 1-Mar-2016
  • (2013)Evolutionary Feature SynthesisMultidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition10.1007/978-3-642-37846-1_10(295-321)Online publication date: 17-Jul-2013
  • (2008)Feature synthesized EM algorithm for image retrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/1352012.13520144:2(1-24)Online publication date: 16-May-2008
  • (2007)Hybrid coevolutionary algorithms vs. SVM algorithmsProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1277057(456-463)Online publication date: 7-Jul-2007
  • (2006)Immune multiobjective optimization algorithm for unsupervised feature selectionProceedings of the 2006 international conference on Applications of Evolutionary Computing10.1007/11732242_43(484-494)Online publication date: 10-Apr-2006

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