nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Aquarium Family Fish Species Identification System Using Deep Neural Networks

verfasst von : Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha, Aboul Ella Hassanien

Erschienen in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, a system for aquarium family fish species identification is proposed. It identifies eight family fish species along with 191 sub-species. The proposed system is built using deep convolutional neural networks (CNN). It consists of four layers, two convolutional and two fully connected layers. A comparative result is presented against other CNN architectures such as AlexNet and VggNet according to four parameters (number of convolution and fully connected layers, the number of epochs in training phase to achieve 100% accuracy, validation accuracy, and testing accuracy). Through the paper, it is proven that the proposed system has competitive results against the other architectures. It achieved 85.59% testing accuracy while AlexNet achieves 85.41% over untrained benchmark dataset. Moreover, the proposed system has less trained images, less memory, less computational complexity in training, validation, and testing phases.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Convolutional Neural Network Model for Emotion Detection from Tweets

Nächstes Kapitel AMS: Adaptive Migration Scheme in Cloud Computing

Fouad, M.M.M., Zawbaa, H.M., El-Bendary, N., Hassanien, A.E.: Automatic Nile Tilapia fish classification approach using machine learning techniques. In: 13th International Conference on Hybrid Intelligent Systems, HIS 2013, pp. 173–178. IEEE (2013)

Fouad, M.M., Zawbaa, H.M., Gaber, T., Snasel, V., Hassanien, A.E.: A fish detection approach based on BAT algorithm. In: The 1st International Conference on Advanced Intelligent System and Informatics, AISI 2015, pp. 273–283. Springer, Beni Suef (2016)

Dominguez, A.: A history of the convolution operation [Retrospectroscope]. IEEE Pulse 6, 38–49 (2015). https://doi.org/10.1109/MPUL.2014.2366903CrossRef

Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998). https://doi.org/10.1109/5.726791CrossRef

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-yMathSciNetCrossRef

Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, pp. 650–657. IEEE (2017)

Strachan, N., Kell, L.: A potential method for the differentiation between haddock fish stocks by computer vision using canonical discriminant analysis. ICES J. Mar. Sci. 52, 145–149 (1995). https://doi.org/10.1016/1054-3139(95)80023-9CrossRef

Rova, A., Rova, A., Mori, G., Dill, L.M.: One fish, two fish, butterfish, trumpeter: recognizing fish in underwater video. In: IAPR Conference on Machine Vision Applications, Tokyo, Japan, pp. 404–407 (2007)

10.

Spampinato, C., Giordano, D., Di Salvo, R., Chen-Burger, Y.-H.J., Fisher, R.B., Nadarajan, G.: Automatic fish classification for underwater species behavior understanding. In: Proceedings of the First ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams—ARTEMIS 2010, p. 45. ACM Press, New York (2010)

11.

Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Selim, I.M.: Deep galaxy: classification of galaxies based on deep convolutional neural networks (2017). arXiv preprint arXiv:1709.02245

12.

Sainath, T.N., Kingsbury, B., Mohamed, A., Ramabhadran, B.: Learning filter banks within a deep neural network framework. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 297–302. IEEE (2013)

13.

Khalifa, N.E., Taha, M.H., Hassanien, A.E., Selim, I.: Deep Galaxy V2: robust deep convolutional neural networks for galaxy morphology classifications. In: 2018 IEEE International Conference on Computing Sciences and Engineering, ICCSE, pp. 122–127. IEEE (2018)

14.

Bui, H.M., Lech, M., Cheng, E., Neville, K., Burnett, I.S.: Object recognition using deep convolutional features transformed by a recursive network structure. IEEE Access 4, 10059–10066 (2017). https://doi.org/10.1109/ACCESS.2016.2639543CrossRef

15.

Scott, G.J., England, M.R., Starms, W.A., Marcum, R.A., Davis, C.H.: Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14, 549–553 (2017). https://doi.org/10.1109/LGRS.2017.2657778CrossRef

16.

Lima, E., Sun, X., Dong, J., Wang, H., Yang, Y., Liu, L.: Learning and transferring convolutional neural network knowledge to ocean front recognition. IEEE Geosci. Remote Sens. Lett. 14, 354–358 (2017). https://doi.org/10.1109/LGRS.2016.2643000CrossRef

17.

Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 2377–2385 (2015)

18.

Anantharajah, K., ZongYuan Ge, McCool, C., Denman, S., Fookes, C., Corke, P., Tjondronegoro, D., Sridharan, S.: Local inter-session variability modelling for object classification. In: IEEE Winter Conference on Applications of Computer Vision, pp. 309–316. IEEE (2014)

19.

Joly, A., Goëau, H., Glotin, H., Spampinato, C., Bonnet, P., Vellinga, W.-P., Planqué, R., Rauber, A., Palazzo, S., Fisher, B., Müller, H.: LifeCLEF 2015: multimedia life species identification challenges. In: Mothe, J., Savoy, J., Kamps, J., Pinel-Sauvagnat, K., Jones, G., San Juan, E., Capellato, L., Ferro, N. (eds.) Experimental IR Meets Multilinguality, Multimodality, and Interaction. Lecture Notes in Computer Science. Springer, Cham (2015)

Titel: Aquarium Family Fish Species Identification System Using Deep Neural Networks
verfasst von: Nour Eldeen M. Khalifa
Mohamed Hamed N. Taha
Aboul Ella Hassanien
Verlag: Springer International Publishing
Buch: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018
Print ISBN: 978-3-319-99009-5

Electronic ISBN: 978-3-319-99010-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-99010-1_32

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"