Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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A Novel Image Classification Method for Content-based Image Retrieval via a Hybrid Genetic Algorithm and Support Vector Machine Approach

  • Received : 2011.08.26
  • Accepted : 2011.09.15
  • Published : 2011.09.30
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Abstract

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

Keywords

References

  1. Fournier, K J., Cord, M. and Philipp-Foliguet, S., "Back-propagation Algorithm for Relevance Feedback in Image retrieval," IEEE ICIP'01, Vol. 1, pp. 686-689, 2001.
  2. Koskela, M., Laaksonen, J. and Oja, E., "Use of Image Subset Features in Image Retrieval with Self- Organizing Maps," LNCS, Vol. 3115, pp. 508-516, 2004.
  3. Park, S. S., Seo, K. K., & Jang, D. S., "Expert system based on artificial neural networks for content-based image retrieval", Expert Systems with Applications, Vol. 29(3), pp. 589-597, 2005. https://doi.org/10.1016/j.eswa.2005.04.027
  4. Tong, K. S. and Chang, E., "Support Vector Machine Active Learning for Image Retrieval," Proc. 9th ACM ICM, pp. 107-118, 2001.
  5. Zhang, L., Lin, F. and Zhang, B., "Support Vector Machine Learning for Image Retrieval," Proc. IEEE ICIP, 2001.
  6. Chang, C. K., Jiang, H., Di, Y., Zhu, D., & Ge, Y., "Time-line based model for software project scheduling with genetic algorithms", Information and Software Technology, Vol. 50(11), 1142-1154, 2008. https://doi.org/10.1016/j.infsof.2008.03.002
  7. Haupt, R. L., "An introduction to genetic algorithms for electromagnetic", IEEE Magazine, Antennas Propagation, Vol. 37, pp. 7-15. 1995.
  8. Vapnik, V., Statistical learning theory. New York: Springer, 1998.
  9. Jain, A. K., & Valilaya, A., "Image retrieval using color and shape", Pattern Recognition, Vol. 29, pp.11244-12233, 1996.
  10. Seo, K.-K., "An Application of One-class Support Vector Machines in Content-based Image Retrieval," Expert Systems with Applications, Vol. 32(2), pp. 491-498, 2007.