Abstract
The good marine ecological environment is the basis for the sustainable development and utilization of marine resources. However, humans have also severely damaged the marine environment while utilizing marine resources. Therefore, image classification for marine pollution is beneficial to the protection and development of the ocean. In recent years, with the rise of convolution neural networks, this algorithm is rarely used in the classification of marine pollutants. This paper will apply the design of 6-layer convolution neural network to image classification of marine pollution (called for short MP-net). Experiments show that Alex net, VGG(11) and MP-net are learning and training in the same data set, and the accuracy rates respectively are 89.17%, 86.25%, and 90.14%. Therefore, in the image classification of marine pollutants using convolution neural networks, the network can adapt to image scenes, automatically learn features, and have good classification results.
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
Clark, R.B.: Marine Pollution, 5th edn. Oxford University Press, Oxford (2002)
Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man Cybern. SMC 13(5), 826–834 (1983)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Le Cun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co- adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Le Cun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004) (2004)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of ICLR 2015 (2015)
Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Metric learning for large scale image classification: generalizing to new classes at near-zero cost. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 488–501. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33709-3_35
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1), 157–173 (2008)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (2010)
LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Computer Vision and Pattern Recognition(CVPR 2004) (2004)
Endres, I., Hoiem, D.: Category independent object proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 575–588. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_42
Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 2071–2084 (2015)
Zeiler, M., Taylor, G., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: Computer Vision and Pattern Recognition (CVPR2011) (2011)
Howard, A.G.: Some improvements on deep convolutional neural network based image classification. In: Proceedings of the ICLR2014 (2014)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_11
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 178–187 (2007)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR2015 (2015)
Acknowledgement
This work is supported by Hainan Provincial Natural Science Foundation of China (project number: 20166235), Hainan provincial university scientific research funding project (project number: Hnky2017-57).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, T., Jia, S., Zhang, H., Zhou, M. (2018). Research on Image Classification of Marine Pollutants with Convolution Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-00021-9_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00020-2
Online ISBN: 978-3-030-00021-9
eBook Packages: Computer ScienceComputer Science (R0)