Journal of Information Processing Systems

  • 제12권4호
  • /
  • Pages.555-576
  • /
  • 2016
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

  • Bekkouche, Ibtissem (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Science and Technology of Oran 'Mohamed Boudiaf') ;
  • Fizazi, Hadria (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Science and Technology of Oran 'Mohamed Boudiaf')
  • 투고 : 2015.02.02
  • 심사 : 2015.05.28
  • 발행 : 2016.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

키워드

참고문헌

  1. A. Messaoud, M. B. Messaoud, A. Kachouri, and F. Sellami, "Classification des arythmies cardiaques par logique floue a partir de signaux ECG," in Proceedings of International Conference on Sciences of Electronic, Technologies of Information and Telecommunications (SETIT2005), Susa, Tunisia, 2005, pp. 1-5.
  2. Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. https://doi.org/10.1177/003754970107600201
  3. F. Glover, "Heuristics for integer programming using surrogate constraints," Decision Sciences, vol. 8, no. 1, pp. 156-166, 1977. https://doi.org/10.1111/j.1540-5915.1977.tb01074.x
  4. O. M. Alia, R. Mandava, D. Ramachandram, and M. E. Aziz, "Harmony search-based cluster initialization for fuzzy c-means segmentation of mr images," in Proceeding of IEEE Region 10 Conference (TENCON 2009-2009), Singapore, 2009, pp. 1-6.
  5. B. Ibtissem and F. Hadria, "Unsupervised clustering of images using harmony search algorithm," Journal of Computer Sciences and Applications, vol. 1, no. 5, pp. 91-99, 2013. https://doi.org/10.12691/jcsa-1-5-3
  6. X. S. Yang, "Harmony search as a metaheuristic algorithm," in Music-Inspired Harmony Search Algorithm. Heidelberg: Springer, 2009, pp. 1-14.
  7. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press, 1992.
  8. D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning. Reading, MA: Addison-Wesley Publishing, 1989.
  9. Z. W. Geem, "Optimal cost design of water distribution networks using harmony search," Engineering Optimization, vol. 38, no. 3, pp. 259-277, 2006. https://doi.org/10.1080/03052150500467430
  10. M. T. Ayvaz, "Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm," Advances in Water Resources, vol. 30, no. 11, pp. 2326-2338, 2007. https://doi.org/10.1016/j.advwatres.2007.05.009
  11. V. M. Romero, L. L. Tomes, and J. P. T. Yusiong, "Tetris agent optimization using harmony search algorithm," International Journal of Computer Science Issues, vol. 8, no. 1, pp. 22-31, 2011.
  12. O. M. Alia, R. Mandava, and M. E. Aziz, "A hybrid harmony search algorithm for MRI brain segmentation," Evolutinary Intelligence, vol. 4, no. 1, pp. 31-49, 2011. https://doi.org/10.1007/s12065-011-0048-1
  13. J. Bezdek, L. Hall, and L. Clarke, "Review of MR image segmentation techniques using pattern recognition," Medical Physics, vol. 20, no. 4, pp. 1033-1048, 1993. https://doi.org/10.1118/1.597000
  14. D. L. Pham, "Spatial models for fuzzy clustering," Computer Vision and Image Understanding, vol. 84, no. 2, pp. 285-297, 2011. https://doi.org/10.1006/cviu.2001.0951
  15. K. L. Wu and M. S. Yang, "A cluster validity index for fuzzy clustering," Pattern Recognition Letters, vol. 26, no. 9, pp. 1275-1291, 2005. https://doi.org/10.1016/j.patrec.2004.11.022
  16. TransformEe de Fourier 2D [Online]. Available: http://tf2d.free.fr/index.php?cours=021_Introduction.
  17. TransformEe de Fourier [Online]. Available: http://www.tsi.enst.fr/pages/enseignement/ressources/mti/ondelettes-2g/francais/Fourier/TF2Dimage.htm.
  18. K. S. Lee, Z. W. Geem, S. H. Lee, and K. W. Bae, "The harmony search heuristic algorithm for discrete structural optimization," Engineering Optimization, vol. 37, no. 7, pp. 663-684, 2005. https://doi.org/10.1080/03052150500211895
  19. M. Mahdavi, M. Fesanghary, and E. Damangir, "An improved harmony search algorithm for solving optimization problems," Applied Mathematics and Computation, vol. 188, no. 2, pp. 1567-1579, 2007. https://doi.org/10.1016/j.amc.2006.11.033
  20. A. T. Khader, A. A. Abusnaina, and Q. Shambour, "Modified tournament harmony search for unconstrained optimisation problems," in Recent Advances on Soft Computing and Data Mining. Heidelberg: Springer, 2014, pp. 283-292.
  21. A. Belmadani, L. Benasla, and M. Rahli, "Etude d'un dispatching economique environnemental par la mEthode harmony search," Acta Electrotehnica, vol. 50, no. 1, pp. 44-48, 2009.
  22. P. Chakraborty, G. G. Roy, S. Das, D. Jain, and A. Abraham, "An improved harmony search algorithm with differential mutation operator," Fundamenta Informaticae, vol. 95, no. 4, pp. 401-426, 2009.
  23. O. M. Alia and R. Mandava, "The variants of the harmony search algorithm: an overview," Artificial Intelligence Review, vol. 36, no. 1, pp. 49-68, 2011. https://doi.org/10.1007/s10462-010-9201-y
  24. R. Y. M. Nakamura, C. R. Pereira, J. P. Papa, and A. X. Falcao, "Optimum-path forest pruning parameter estimation through harmony search," in Proceedings of 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Maceio, Brazil, 2011, pp. 181-188.
  25. I. Bekoouche and H. Fizazi, "Conception d'une mEthode bio-inspirEe 'Harmony Search' pour les traitements des images satellitaires," in Proceedings of the 1st International Conference on New Technologies and Communication (ICNTC), Chlef, Algeria, 2012.
  26. I. Bekoouche and H. Fizazi, "New conception of algorithm 'Harmony Search' for the unsupervised clustering of images," in Proceedings of the 1st Conference on Theoretical and Applicative Aspects of Computer Science (CTAACS), Skikda, Algeria, 2012.
  27. K. S. Lee and Z. W. Geem, "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice," Computer Methodes in Applied Mechanics and Engineering, vol. 194, no. 36-38, pp. 3902-3933, 2005. https://doi.org/10.1016/j.cma.2004.09.007
  28. E. Y. Tak Ma, "A replication of harmony fuzzy image segmentation algorithm," CIS 6050 - Artificial Neural Networks, University of Guelph, Canada, 2011.
  29. H. Fizazi and B. Wafaa, "Contribution des OC-SVM a l'Extraction des Zones de DEgats Sismiques," in Proceedings of the 1st International Conference on Information Systems and Technologies (ICIST), Tebessa, Algeria, 2011.
  30. I. Bekkouche and H. Fizazi, "Fuzzy harmony search for unspervised clustering of the images," in Proceedings of the 1st International Conference on Information Systems and Technologies (ICIST), Tangier, Morocco, 2013.