Abstract
We use an hybrid approach based on a genetic algorithm and on the gradient descent method in order to decompose an image. In the pre-processing phase the genetic algorithm is used for finding two suitable initial families of fuzzy sets that decompose R in accordance to the well known concept of Schein rank. These fuzzy sets are successively used in the descent gradient algorithm which determines the final fuzzy sets, useful for the reconstruction of the image. The experiments are executed on some images extracted from the the SIDBA standard image database.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Di Martino, F., Sessa, S.: A Genetic Algorithm Based on Eigen Fuzzy Sets for Image Reconstruction. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 342–348. Springer, Heidelberg (2007)
Nobuhara, H., Hirota, K., Pedrycz, W.: Fast Solving Method of Fuzzy Relational Equations and its Application to Lossy Image Compression. IEEE Transactions of Fuzzy Systems 8(3), 325–334 (2000)
Nobuhara, H., Takama, Y., Hirota, K.: Fast Iterative Solving Method of Various Types of Fuzzy Relational Equations and its Application to Image Reconstruction. Internat. J. Adv. Comput. Intell. 5(2), 90–98 (2001)
Nobuhara, H., Bede, B., Hirota, K.: On Various Eigen Fuzzy Sets and their Application to Image Reconstruction. Information Sciences 176, 2988–3010 (2006)
Di Nola, A., Pedrycz, W., Sessa, S.: When is a Fuzzy Relation Decomposable in Two Fuzzy Sets? Fuzzy Sets Syst. 16, 87–90 (1985)
Di Nola, A., Pedrycz, W., Sessa, S.: Decomposition Problem of Fuzzy Relations. Internat. J. Gen. Syst. 10, 113–123 (1985)
Pedrycz, W.: Optimization Schemes for Decomposition of Fuzzy Relations. Fuzzy Sets Syst 100, 301–325 (1998)
Nobuhara, H., Hirota, K., Pedrycz, W., Sessa, S.: Two Iterative Methods of Decomposition of a Fuzzy Relation for Image Compression/Decomposition Processing. Soft. Comput. 8(10), 698–704 (2004)
Nobuhara, H., Hirota, K., Pedrycz, W., Sessa, S.: Efficient Decomposition Method of Fuzzy Relation and Their Application to Image Decomposition. Applied Soft Computing 5, 399–408 (2005)
Pedrycz, W., Hirota, K., Sessa, S.: A Decomposition of Fuzzy Relations. IEEE Transactions on Systems, Man and Cybernetics 31(4), 657–663 (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)
Pedrycz, W., Reformat, M.: Genetic Optimization with Fuzzy Coding. In: Herrera, F., Verdegay, J. (eds.) Genetic Algorithms and Soft Computing. Studies in Fuzziness and Soft Computing. Studies in Fuzziness and Soft Computing, vol. 8, pp. 51–67. Springer, Berlin Heidelberg New York (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Martino, F., Loia, V., Sessa, S. (2008). A Fuzzy Hybrid Method for Image Decomposition Problem. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-78761-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78760-0
Online ISBN: 978-3-540-78761-7
eBook Packages: Computer ScienceComputer Science (R0)