A Hybrid Memetic Algorithm (Genetic Algorithm and Tabu Local Search) with Back-Propagation Classifier for Fish Recognition

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In this research our goal is to develop a hybrid approach for optimizing and enhancing back- propagation classifier (BPC) performance using a Memetic Algorithm (Genetic algorithm and Tabu Local Search), thus; Memetic Algorithm used to tune the parameters of the BPC. The proposed hybrid approach (HGATS-BPC) was tested based on fish images recognition. To recognize the pattern of interest (fish object) in the image based on extracted features from color signature. Histogram technique and Gray Level Co-occurrence Matrix (GLCM) used to extract 20 features from fish images based on color signature. The general BPC has several issues to be optimized by Memetic Algorithm such as speed, and easy for running into local minimum. In our study we used 800 fish images for 20 different fish families; each family has a different number of fish types. These images are divided into two datasets: 560 training images and 240 testing images. HGATS-BPC successfully optimized and enhanced the performance of the BPC in term of accuracy and outperformed the traditional BPC and previous methodologies by obtaining more accurate results but with a high cost of computational time compared to the BPC. The overall accuracy obtained by BPC was 85%, while the HGATS-BPC obtained 94% based on 800 fish images. Finally; the HGATS-BPC classifier is able to classify the given fish images into poisonous or non-poisonous fish and classify the poisonous and non-poisonous fish into its family.
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A Hybrid Memetic Algorithm; Back-Propagation Classifier; Genetic Algorithm; Tabu Local Search; Histogram Technique; Gray Level Co-Occurrence Matrix (GLCM); Color Signature; Digital Fish Images; Poisonous And Non-Poisonous Fish

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M. Alsmadi, K.B. Omar, S.A. Noah and I. Almarashdeh, A Hybrid Memetic Algorithm with Back-propagation Classifier for Fish Classification Based on Robust Features Extraction from PLGF and Shape Measurements. Information Technology Journal, Vol.10, pp. 944-954, 2011.

Bai, X., X. Yang and J.L. Latecki, Detection and recognition of contour parts based on shape similarity. Philadelphia, USA, “Pattern Recognition”, Vol. 41, pp. 2189-2199, 2008.

Kim, J.S. and K.S. Hong, Color-texture segmentation using unsupervised graph cuts, Republic of Korea. “Pattern Recognition”, Vol. 42, pp. 735-750, 2009.

Nery, M.S., A.M. Machado, M.F.M. Campos, F.L.C.P. Dua and R. Carceroni et al, determining the appropriate feature set for fish classification tasks. “Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’05)”, Vol. 5, pp. 1530-1834, 2006.

Lee, D.J., R. Schoenberger, D. Shiozawa, X. Xu and P. Zhan, Contour matching for a fish recognition and migration monitoring system. “In: D.-J. Lee et al.: Contour matching for a fish recognition and migration monitoring system, Studies in Computational Intelligence (SCI)”, Vol. 122, pp. 183-207, 2008.

Larsen, R., Olafsdottir, H. And Ersbøll, B. K, Shape and Texture Based Classification of Fish Species. Dlm. (pnyt.). Image Analysis, pp. 745–749. Springer Link 2009.

Reddy, A. R., Rao, B. S., Rao, G. S. N. And Nagaraju, C. A Novel Method for Aquamarine Learning Environment for Classification of Fish Database. International Journal of Computer Science & Communication Vol. 1, n. 1, pp. 87-89, 2010.

Rodrigues, M. T. A., P´adua, F. a. L. C., Gomes, R. e. M. and Soares, G. E, Automatic Fish Species Classification Based on Robust Feature Extraction Techniques and Artificial Immune Systems. Intelligent Systems Laboratory, Federal Center of Technological Education of Minas Gerais, Av. Amazonas, 7675, Belo Horizonte, MG, Brasil, 2010.

Muñiz, R. And Corrales, J. e. A, Novel Techniques for Color Texture Classification. Proceedings of the International Conference on Image Processing, Computer Vision, Pattern Recognition, Las Vegas, Nevada, USA, (Page: 26-29, year of Publication: 2006).

Arivazhagan, S., Ganesan, L. and Angayarkanni, V. Color Texture Classification Using Wavelet Transform. Computational Intelligence and Multimedia Applications, International Conference on, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), Las Vegas, Nevada. (Page: 315-320, Year of Publication: 2005).

Akhloufi, M. A., Ben Larbi, W. And Maldague, X. Framework for color-texture classification in machine vision inspection of industrial products. Systems, Man and Cybernetics, ISIC. IEEE International Conference (Page: 1067-1071 Year of Publication: 2007).

Mirmehdi, M. And Petrou, M. Segmentation of color textures. Pattern Analysis and Machine Intelligence, IEEE Transactions (Page: 142-159, Year of Publication: 2000).

Maenpaa, T., Pietikainen, M. And Viertola, J. Separating color and pattern information for color texture discrimination. Pattern Recognition Proceedings. 16th International Conference (Page: 668-671, Year of Publication: 2002).

Michalewicz, Z. Genetic algorithms + data structures = evolution programs (3nd, extended ed.). New York, NY, USA: Springer-Verlag New York, Inc, 1996.

Goldberg, D, Genetic Algorithms in Search, Optimization, and Machine Learning.New York: Addison-Wesley, 1989.

Glover, F, Tabu search - part i. INFORMS Journal on Computing Vol. 1, n. 3, pp. 190–206, 1989.

Glover, F. & Laguna, M, Tabu Search. Kluwer Academic Publishers, Dordrecht/Boston/London, 1997.

Glover, F, Tabu search - part ii. INFORMS Journal on Computing Vol. 2, n. 1, pp. 4–32, 1990.

Blazewicz, J., Glover, F. & Kasprzak, M, Dna sequencing - tabu and scatter search combined. INFORMS Journal on Computing Vol. 16, n. 3, pp. 232–240, 2004.

Galinier, P. & kao Hao, J. Hybrid Evolutionary Algorithms for Graph Coloring, 1998.

Blesa, M. J., Blum, C., Gaspero, L. D., Roli, A., Samples, M. & Schaerf, A., Hybrid Metaheuristics, 6th International Workshop, Udine, Italy, October 16-17, Proceedings, Lecture Notes in Computer Science, Springer. (Page: 237 Year of Publication: 2009)

Moscato, P, Memetic algorithms: a short introduction pp. 219–234, 1999.

Tan, K. C., Lee, T. H., Khoo, D. & Khor, E. F, A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B 31 (4), (Page: 537–556 Year of Publication: 2001)

Ahn, Y., Park, J., Lee, C.-G., Kim, J.-W. & Jung, S.-Y, Novel memetic algorithm implemented with ga (genetic algorithm) and mads (mesh adaptive direct search) for optimal design of electromagnetic system. Magnetics, IEEE Transactions on (Page: 1982 –1985 Year of Publication: 2010)

Burke, E. & Landa Silva, J, The design of memetic algorithms for scheduling and timetabling problems. Vol. 166, pp. 289-312, Springer, 2004.


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