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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 426957, 10 pages
http://dx.doi.org/10.5402/2012/426957
Research Article

Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

1Department of Computer Science and Engineering, Kumaraguru College of Technology, Tamil Nadu, Coimbatore 641049, India
2Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, Coimbatore 641004, India

Received 9 September 2011; Accepted 24 October 2011

Academic Editor: C. Chen

Copyright © 2012 D. Chandrakala and S. Sumathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Gong, H. Zhang, H. C. Chuan, and M. Sakauchi, “Image database system with content capturing and fast image indexing abilities,” in Proceedings of the International Conference on Multimedia Computing and Systems, pp. 121–130, Boston, Mass, USA, May 1994.
  2. P. Muneesawang and L. Guan, “An interactive approach for CBIR using a network of radial basis functions,” IEEE Transactions on Multimedia, vol. 6, no. 5, pp. 703–716, 2004. View at Publisher · View at Google Scholar
  3. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: ideas, influences, and trends of the new age,” ACM Computing Surveys, vol. 40, no. 2, article 5, pp. 1–60, 2008. View at Publisher · View at Google Scholar
  4. D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Yue, Z. Li, L. Liu, and Z. Fu, “Content-based image retrieval using color and texture fused features,” Mathematical and Computer Modelling, vol. 54, no. 3-4, pp. 1121–1127, 2011. View at Publisher · View at Google Scholar
  6. B. G. Prasad, K. K. Biswas, and S. K. Gupta, “Region-based image retrieval using integrated color, shape, and location index,” Computer Vision and Image Understanding, vol. 94, no. 1–3, pp. 193–233, 2004.
  7. Y. D. Chun, N. C. Kim, and I. H. Jang, “Content-based image retrieval using multiresolution color and texture features,” IEEE Transactions on Multimedia, vol. 10, no. 6, Article ID 4657457, pp. 1073–1084, 2008. View at Publisher · View at Google Scholar
  8. X. Y. Tai and L. D. Wang, “Medical image retrieval based on color-texture algorithm and GTI model,” in Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE '08), pp. 2574–2578, Shanghai, China, May 2006. View at Publisher · View at Google Scholar
  9. S. Liapis and G. Tziritas, “Color and texture image retrieval using chromaticity histograms and wavelet frames,” IEEE Transactions on Multimedia, vol. 6, no. 5, pp. 676–686, 2004. View at Publisher · View at Google Scholar
  10. H. Permuter, J. Francos, and I. H. Jermyn, “Gaussian mixture models of texture and colour for image database retrieval,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (ICASSP '03), vol. 3, pp. 569–572, Hong Kong, April 2003. View at Publisher · View at Google Scholar
  11. W. Niblack, R. Barber, W. Equitz et al., “QBIC project: querying images by content, using color, texture, and shape,” in Proceedings of the Storage and Retrieval for Image and Video Databases, (SPIE 1908), pp. 173–187, San Jose, Calif, USA, February 1993.
  12. A. Pentland, R.W. Picard, and S. Scarloff, “Photbook: tools for content based manipulation of image databases,” in Proceedings of the International Socitey for Optics and Photonics (SPIE 2185), pp. 34–47, San Jose, Calif, USA, 1994.
  13. S. Mehrotra, Y. Rui, M. Ortega-Binderberger, and T. S. Huang, “Supporting content-based queries over images in MARS,” in Proceedings of the IEEE International Conference on Multimedia Computing and Systems (ICMCS '97), pp. 632–633, June 1997.
  14. J. R. Bach, C. Fuller, A. Gupta et al., “Virage image search engine: an open framework for image management,” in Proceedings of the Storage and Retrieval for Still Image and Video Databases (SPIE 2670), pp. 76–87, San Jose, Calif, USA, February 1996.
  15. J. R. Smith, Integrated spatial and feature image systems: retrieval, analysis and compression, Ph.D. thesis, Columbia University, New York, NY, USA, 1997.
  16. J. Z. Wang, J. Li, and G. Wiederhold, “SIMPLIcity: semantics-sensitive integrated matching for picture libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947–963, 2001. View at Publisher · View at Google Scholar
  17. Y. Rui, T. S. Huang, and S. F. Chang, “Image retrieval: current techniques, promising directions, and open issues,” Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39–62, 1999. View at Publisher · View at Google Scholar
  18. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
  19. Y. D. Chun, S. Y. Seo, and N. C. Kim, “Image retrieval using BDIP and BVLC moments,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 9, pp. 951–957, 2003. View at Publisher · View at Google Scholar