Optimal reconstruction and recognition of images by Jacobi Fourier moments and artificial bee colony (ABC) algorithm
Keywords:
Orthogonal Jacobi polynomials, Generalized Jacobi Fourier moments, images reconstruction, Artificial bee Colony (ABC)
Abstract
The orthogonal moments giving relevant results of these last years within the framework of object detection, pattern recognition and image reconstruction, this work based on orthogonal functions called Orthogonal Jacobi Polynomials (OJPs), and we introduce a new set of moments called Generalized Jacobi Fourier Moments (GJFMs), these polynomials are characterized by parameters . However, it was very important to optimize these parameters in order to obtain a good result, in this context; this study used a new approach to optimized Jacobi Fourier parameters using the artificial bee colony algorithm (ABC) in order to improves the quality of reconstruction of images of large sizes. On the one hand, to validate this technique which offers a high image reconstruction quality. On other hand, the comparison carried out with other algorithms clearly indicates the advantage of the proposed method.References
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[2] G. Papakostas, “Over 50 Years of Image Moments and Moment Invariants”, Moments and Moment Invar-iants-Theory and Applications, p. 3-32, 2014. (ISBN 978-618-81418-0-3).
[3] J. Flusser, T. Suk, B. Zitova, “2D and 3D Image Analysis by Moments”, John Wiley & Sons, (2016). (ISBN: 9781119039372).
[4] S. Gishkori and B. Mulgrew, “Pseudo-Zernike Moments Based Sparse Representations for SAR Image Clas-sification”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 55 (2), p. 1037-1044, 2019.
[5] H. R. Kanan, S. Salkhordeh , “Rotation invariant multi-frame image super resolution reconstruction using Pseudo Zernike Moments”, Signal Processing, Vol. 118, p. 103-114, 2016.
[6] C. Singh, A. Aggarwal, “Single-image super-resolution using orthogonal rotation invariant moments”, Com-puters & Electrical Engineering, Vol. 62, , p. 266-280, 2017.
[7] Y. Li, F. Li, K. Yang, C. Price and Q. Shen, “Remote sensing image registration based on Gaussian-Hermite moments and the PseudoRANSAC algorithm”, Remote Sensing Letters, Vol. 8 (12), p. 1162- 1171, 2017.
[8] Oliveira, PA., Cintra, RJ., Bayer, FM., Kulasekera, S., Madanayake, A.: Low-complexity image and video coding based on an approximate discrete tchebichef transform. IEEE Trans Circuits Syst Video Technol. 27(5):1066–76 (2016).
[9] K. M. Hosny, “Image representation using accurate orthogonal Gegenbauer moments,” Pattern Recognition Letter, Vol. 32(6), p.795–804, 2011.
[10] X. Wang, T. Yang, F. Guo, “Image analysis by circularly semiorthogonal moments”, Pattern Recognition, Vol. 49, p. 226 236, 2016.
[11] I. Batioua, R. Benouini, K. Zenkouar, A. Zahia, E. F. Hakim, “3D image analysis by separable discrete or-thogonal moments based on Krawtchouk and Tchebichef polynomials”, Pattern Recognition, Vol. 71, p. 264-277, 2017.
[12] K. M. Hosny and M. M. Darwish “New set of Quaternion Moments for Color Images Representation and Recognition” Journal of Mathematical Imaging and Vision, Vol. 60 (5), 717-736, 2018.
[13] C. L. Lim, R. Paramesran, W. A. Jassim, Y. P.Yu, K. N. Ngan, “Blind image quality assessment for Gaussian blur images using exact2
[14] Ernawan, F., Kabir, N., Zamli, KZ.: An efficient image compression technique using Tchebichef bit allocation. Optik (Stuttg). 148 :106–19 (2017).
[15] Z. Ping, H. Ren, J. Zou, Y. Sheng, W. Bo, “Generic orthogonal moments: Jacobi–Fourier moments for invar-iant image description”, Pattern Recognition, 40 (4), p. 1245-1254, 2007.
[16] T. V. Hoang, S. Tabbone, “Errata and comments on Generic orthogonal moments: Jacobi–Fourier mo-ments for invariant image description”, Pattern Recognition, Vol. 46, p. 3148–3155, 2013.
[17] A. Khotanzad, and Y. H. Hong, “Invariant Image Recognition by Zernike Moments”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 12(5), p. 489–497, 1990.
[18] Bailey R. R. and Srinath M. D., "Orthogonal moment features for use with parametric and non-parametric classifiers", IEEE Trans. Pattern Anal. Mach. Intell. Vol. 18(4), pp. 389-399, 1996.
[19] Y. L. Sheng, L. X. Shen, “Orthogonal Fourier–Mellin moments for invariant pattern recognition”, Journal of the Optical Society of America A, Vol. 11(6), p. 1748–1757, 1994.
[20] Zi Liang Ping, Ri Geng Wu, Yun Long Sheng “Image description with Chebyshev–Fourier moments”, Jour-nal of Optical Society of America: A, Vol. 19 (9), p. 1748-1754, 2002.
[21] Guleng Amu, Surong Hasi, Xingyu Yang, and Ziliang Ping, “Image analysis by pseudo-Jacobi (p = 4, q = 3)–Fourier moments”, Applied Optics, Vol. 43(10), p. 2093-2101, 2004.
[22] C. Camacho-Bello, J. J. B´aez-Rojas, C. Toxqui-Quitl, A. PadillaVivanco, “Color image reconstruction using quaternion LegendreFourier moments in polar pixels”, Proceedings of the International Conference on Mecha-tronics, Electronics and Automotive Engineering, p.3-8, 2014.
[23] Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
[24] Liao SX, Pawlak M (1996) On image analysis by moments. IEEE Trans Pattern Anal Mach Intell 18(3):254–266. https://doi.org/10.1109/34.485554
[25] Regularized Jacobi Wavelets Kernel for Support Vector Machines | Statistics, Optimization & Information Computing (iapress.org)
[26] Clustering using firefly algorithm: Performance study - ScienceDirect
[27] View of A new routing method based on ant colony optimization in vehicular ad-hoc network (iapress.org)
[28] Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Re-view and Taxonomy | IEEE Journals & Magazine | IEEE Xplore
[29] (PDF) A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global nu-merical optimization | Statistics, Optimization & Information Computing (iapress.org)
[30] A Hybrid Harmony Search and Particle Swarm Optimization Algorithm (HSPSO) for Testing Non-functional Properties in Software System | Statistics, Optimization & Information Computing (iapress.org)
[31] VIS @ ETH Zurich – Visual Intelligence and Systems | ETH Zurich n.d. https://cv.ethz.ch/ (accessed April 27, 2023).
[32] Columbia Object Image Library (COIL-100). Academic Torrents n.d. https://academictorrents.com/details/ce39e4554b2207c7764a58acf190dd3ccfa227e2 (accessed April 27, 2023)
[33] Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
[34] Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
[35] Yazdani M, Jolai F (2016) Lion optimization algorithm (loa): a nature-inspired metaheuristic algorithm. J Com-put Des Eng 3(1):24–36
[36] Wang GG, Deb S, Coelho LdS (2015) Elephant herding optimization. In: 2015 3rd international symposium on co putational and business intelligence (ISCBI). IEEE, pp 1–5
Published
2024-02-21
How to Cite
Yahya, S., EL-Mekkaoui, J., BENSLIMANE, M., Janati Idrissi, B., El Ogri, O., & hjouji, A. (2024). Optimal reconstruction and recognition of images by Jacobi Fourier moments and artificial bee colony (ABC) algorithm. Statistics, Optimization & Information Computing, 12(3), 829-840. https://doi.org/10.19139/soic-2310-5070-1973
Section
Research Articles
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