International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.13, No.1, Feb. 2021

Content-based Fish Classification Using Combination of Machine Learning Methods

Full Text (PDF, 983KB), PP.62-68

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S.M. Mohidul Islam, Suriya Islam Bani, Rupa Ghosh

Index Terms

Content;Global feature;Local texture;Combined Model;Fish species


Fish species recognition is an increasing demand to the field of fish ecology, fishing industry sector, fisheries survey applications, and other related concerns. Traditionally, concept-based fish specifies identification procedure is used. But it has some limitations. Content-based classification overcomes these problems.  In this paper, a content-based fish recognition system based on the fusion of local features and global feature is proposed. For local features extraction from fish image, Local Binary Pattern (LBP), Speeded-Up Robust Feature (SURF), and Scale Invariant Feature Transform (SIFT) are used. To extract global feature from fish image, Color Coherence Vector (CCV) is used. Five popular machine learning models such as: Decision Tree, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN) are used for fish species prediction.  Finally, prediction decisions of the above machine learning models are combined to select the final fish class based on majority vote. The experiment is performed on a subset of ‘QUT_fish_data’ dataset containing 256 fish images of 21 classes and the result (accuracy 98.46%) shows that though the proposed method does not outperform all existing fish classification methods but it outperforms many existing methods and so, the method is a competitive alternative in this field. 

Cite This Paper

S.M. Mohidul Islam, Suriya Islam Bani, Rupa Ghosh, "Content-based Fish Classification Using Combination of Machine Learning Methods", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.1, pp.62-68, 2021. DOI: 10.5815/ijitcs.2021.01.05


[1]F. Long, H. J. Zhang and D. D. Feng, Fundamentals of Content-basedImage Retrieval, In Multimedia Information Retrieval andManagement, D. Feng Eds, Springer, 2003.

[2]A. Goyal and E. Walia, “Variants of Dense Descriptors and Zernike Moments as Features for Accurate Shape-based Image Retrieval,” Signal, Image Video Processing, pp. 1–17, 2014.

[3]A. Goyal and E. Walia, “An Analysis of Shape Based Image Retrieval Using Variants of Zernike Moments as Features,” Int. J. Imaging Robot. [Formerly known as “International J. Imaging” (ISSN 0974-0627)] Vol. 7; Issue No. 1; Year 2012; Int. J. Imag. Robot. ISSN 2231–525X; Copyr. © 2012 by IJIR (CESER Publ., vol. 7, 2012.

[4]K. Anantharajah, Z. Y. Ge, C. McCool, S. Denman, C. B. Fookes, P. Corke, D. W. Tjondronegoro, and S. Sridharan, “Local inter-session variability modelling for object classification,” 2014.

[5]M. Avic and M. Sarigul, “Comparison of Different Deep Structures for Fish Classification”. In International Journal of Computer Theory and Engineering, Vol. 9, No. 5, October 2017.


[7]Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. "Speeded-up robust features (SURF)." Computer vision and image understanding 110, no. 3 (2008): 346-359.


[9]Lowe, David G. "Object recognition from local scale-invariant features." In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol. 2, pp. 1150-1157. Ieee, 1999.

[10]Khan, Nabeel Younus, Brendan McCane, and Geoff Wyvill. "SIFT and SURF performance evaluation against various image deformations on benchmark dataset." In 2011 International Conference on Digital Image Computing: Techniques and Applications, pp. 501-506. IEEE, 2011.




[14]Jaiwei Han, Micheline Kamber, Jian Pei, “Data Mining Conecpts and Techniques”, Morgan Kaufmann, Third Edition.


[16]Christopher M. “BishopPattern Recognition and Machine Learning”, 2006.

[17]Mutasem Khalil Sari Alsmadi, Prof.Dr Khairuddin Bin Omar , Prof.Dr. Shahrul Azman Noah and Ibrahim Almarashdah, ”Fish recognition based on the combination between robust features selection, image segmentation and geometrical parameters techniques using artificial neural network and decision tree” , (IJCSIS) International Journal of Computer Science and Information Security, Vol.6, No. 2, 2009.

[18]Kho Geok Hond, “Fish species recognition system based on chain code representation of shapes”, Universiti Teknologi Malaysia, May 19, 2011.

[19]C. Spampinato, D. Giordano, R. Di Salvo, Y.-H. J. Chen-Burger, R. B. Fisher, and G. Nadarajan, "Automatic fish classification for underwater species behavior understanding," in 1st ACM International Workshop on ARTEMIS, 2010, pp. 45-50.

[20]P. X. Huang, B. J. Boom, and R. B. Fisher, "Hierarchical classification with reject option for live fish recognition," Machine Vision and Applications, vol. 26, no. 1, pp. 89-102, 2014.