- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Submit a Manuscript
- Table of Contents
ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 426957, 10 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- J. R. Smith, Integrated spatial and feature image systems: retrieval, analysis and compression, Ph.D. thesis, Columbia University, New York, NY, USA, 1997.
- 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.
- 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.
- 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.
- 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.