Abstract
The classification of the fish silhouettes allows a quick decision of the fish species presence and amount in the given scene. The classical approach of the machine learning is used to test the question of linear separability of fish species silhouettes classes. The preprocessing of images consisted of object to background segmentation and image registration. The classificator is trained using modified Rosenblatt algorithm for loss function of discriminant analysis.
This article is disseminating the preliminary results of training and testing of six fish species classification. The images were of different quality and light conditions. The classificator with the possibility to undecide is introduced and compared. The results are discussed from the point of view of usability of classical methods, preprocessing conditioning, and parametrization of loss function.
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
Similar content being viewed by others
References
Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18, 1–8 (1998)
Bothmann, L., Windmann, M., Kauermann, G.: Realtime classification of fish in underwater sonar videos. J. R. Stat. Soc. Ser. C (Appl. Stat.) 65(4), 565–584 (2016)
Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. (CSUR) 24(4), 325–376 (1992)
Cadieux, S., Michaud, F., Lalonde, F.: Intelligent system for automated fish sorting and counting. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No. 00CH37113), vol. 2, pp. 1279–1284. IEEE (2000)
Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2016)
Elizondo, D.: The linear separability problem: some testing methods. IEEE Trans. Neural Netw. 17(2), 330–344 (2006)
Fainzilberg, L., Matushevych, N.: Comparative evaluation of convergence’s speed of learning algorithms for linear classifiers by statistical experiments method. Kibernetika i vychislitelnaya technika (2018)
Haghighat, M., Li, X., Fang, Z., Zhang, Y., Negahdaripour, S.: Segmentation, classification and modeling of two-dimensional forward-scan sonar imagery for efficient coding and synthesis. In: OCEANS 2016 MTS/IEEE Monterey, pp. 1–8. IEEE (2016)
Kouba, A., Sales, J., Sergejevová, M., Kozák, P., Masojídek, J.: Colour intensity in angelfish (p terophyllum scalare) as influenced by dietary microalgae addition. J. Appl. Ichthyol. 29(1), 193–199 (2013)
Li, D., Hao, Y., Duan, Y.: Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. Rev. Aquac. (2019). https://doi.org/10.1111/raq.12388
Lundova, K., et al.: The effects of a prolonged photoperiod and light source on growth, sexual maturation, fin condition, and vulnerability to fungal disease in brook trout salvelinus fontinalis. Aquac. Res. 50(1), 256–267 (2019)
Maintz, J.A., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)
Murty, M.N., Raghava, R.: Linear discriminant function. Support Vector Machines and Perceptrons. SCS, pp. 15–25. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41063-0_2
Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A., Urban, J.: Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquac. 9(4), 369–387 (2017)
Shortis, M.: Camera calibration techniques for accurate measurement underwater. In: McCarthy, J.K., Benjamin, J., Winton, T., van Duivenvoorde, W. (eds.) 3D Recording and Interpretation for Maritime Archaeology. CRL, vol. 31, pp. 11–27. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03635-5_2
Shortis, M.R., Ravanbakhsh, M., Shafait, F., Mian, A.: Progress in the automated identification, measurement, and counting of fish in underwater image sequences. Mar. Technol. Soc. J. 50(1), 4–16 (2016)
Siddiqui, S.A., et al.: Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES J. Mar. Sci. 75(1), 374–389 (2018)
Sklansky, J., Wassel, G.N.: Linearly separable classes. In: Sklansky, J., Wassel, G.N. (eds.) Pattern Classifiers and Trainable Machines, pp. 31–78. Springer, New York (1981). https://doi.org/10.1007/978-1-4612-5838-4_2
Strachan, N.J.C., Nesvadba, P., Allen, A.R.: Fish species recognition by shape analysis of images. Pattern Recogn. 23(5), 539–544 (1990)
Urban, J.: Colormetric experiments on aquatic organisms. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 96–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56148-6_8
Urban, J., Štys, D., Sergejevová, M., Masojídek, J.: Expertomica fishgui: comparison of fish skin colour. J. Appl. Ichthyol. 29(1), 172–180 (2013)
Verschae, R., Kawashima, H., Nobuhara, S.: A multi-camera system for underwater real-time 3D fish detection and tracking. In: OCEANS 2017-Anchorage, pp. 1–5. IEEE (2017)
Viergever, M.A., Maintz, J.A., Klein, S., Murphy, K., Staring, M., Pluim, J.P.: A survey of medical image registration-under review (2016)
Zat’ková, I., Sergejevová, M., Urban, J., Vachta, R., Štys, D., Masojidek, J.: Carotenoid-enriched microalgal biomass as feed supplement for freshwater ornamentals: albinic form of wels catfish (silurus glanis). Aquac. Nutr. 17(3), 278–286 (2011)
Zhang, D., Lee, D.J., Zhang, M., Tippetts, B.J., Lillywhite, K.D.: Object recognition algorithm for the automatic identification and removal of invasive fish. Biosyst. Eng. 145, 65–75 (2016)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)
Acknowledgments
This research was supported by the Ministry of Education, Youth and Sports of the Czech Republic project No. LO1506. The study was financially supported by the Ministry of Education, Youth and Sports of the Czech Republic - project CENAKVA (LM2018099), the CENAKVA Centre Development (No.CZ.1.05/2.1.00/19.0380). Authors thank to J.Urban for discussion and consultation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Urbanova, P., Bozhynov, V., Císař, P., Železný, M. (2020). Classification of Fish Species Using Silhouettes. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)