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FDCNet: filtering deep convolutional network for marine organism classification

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Abstract

Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).

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Reference

  1. Arora S, Bhaskara A, Ge R, and Ma T (2013) Provable bounds for learning some deep representations, CoRR, abs/1310.6343

  2. Bell R, Koren Y (2007) Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter 9(2):75–79

    Article  Google Scholar 

  3. Chiang JY, Chen YC (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769

    Article  MathSciNet  MATH  Google Scholar 

  4. Deza E, and Deza M, Encyclopedia of distances Springer book, pp.1–583

  5. Dong C, Loy C, He K, and Tang X 2014 Learning a deep convolutional network for image super-resolution,” In Computer Vision –ECCV, pp.184–199

  6. Fattal R (2014) Dehazing using color-lines. ACM Trans Graphics 34(1):1–10

    Article  Google Scholar 

  7. Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Trans Image Process 21(2):662–673

    Article  MathSciNet  MATH  Google Scholar 

  8. Girshick R, Donahue J, Darrell T and Malik J 2014 Rich feature hierarchies for accurate object detection and semantic segmentation, In Proc. Of IEEE Conf Comput Vis Pattern Recognit

  9. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  10. He K, Zhang X, Ren S, Sun J, (2016) Deep residual learning for image recognition In Proc. Of IEEE Conf Comput Vis Pattern Recognit, pp.1–12

  11. Joachims T (2006) Training linear SVMs in linear time, In Proc Of ACM KDD pp.1–10

  12. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, and Li F, 2014 Large-scale video classification with convolutional neural networks, In Proc Of IEEE Conf Comput Vis Pattern Recognit, pp.1725–1732

  13. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  14. Lee Y, Gibson K, Lee Z, and Nguyen T, (2014) Stereo image defogging, In Proc. of IEEE ICIP, pp.5427–5431

  15. Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77

    Article  Google Scholar 

  16. Liu F, Shen C, Lin G, Reid I (2016) Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans Pattern Anal Mach Intell 38(10):2024–2039

    Article  Google Scholar 

  17. Long J, Shelhamer E, and Darrel T 2015 Fully convolutional networks for semantic segmentation, In Proc. Of IEEE Conf Comput Vis Pattern Recognit, pp.3431–3440

  18. Lu H, Li Y, Zhang L, Serikawa S (2015a) Contrast enhancement for images in turbid water. Journal of Optical Society of America A 32(5):886–893

    Article  Google Scholar 

  19. Lu H, Li Y, and Serikawa S (2015b) Single underwater image descattering and color correction, In Proc. of IEEE Acoust Speech Signal Process, pp.1–5

  20. Lu H, Li B, Zhu J, Li Y, Li Y, He L, Li J, and Serikawa S 2016a Wound intensity correction and segmentation with convolutional neural networks, Concurrency Comput: Prac Experience, pp.1–10

  21. Lu H, Li Y, Nakashima S, Serikawa S (2016b) Turbidity underwater image restoration using spectral properties and light compensation. IEICE Trans Inf Syst E99D(1):219–227

    Article  Google Scholar 

  22. Maji S, Berg A, Malik J (2013) Efficient classification for additive kernel SVMs. IEEE Trans Pattern Anal Mach Intell 35(1):66–77

    Article  Google Scholar 

  23. Nicholas C-B, Anush M, and Eustice RM (2010) Initial results in underwater single image dehazing, In Proc. of IEEE OCEANS, pp. 1–8

  24. Ren J, and Xu L 2015 On vectorization of deep convolutional neural networks for vision tasks, In Proc Of AAAI Artif Intell, pp.1840–1846

  25. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Computers & Electrical Engineering 40(1):41–50

    Article  Google Scholar 

  26. Simonyan K, and Zisserman A (2015) Very deep convolutional networks for large-scale image recognition, In Proc. Of IEEE ICLR2015, pp.1–14

  27. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A 2015 Going deeper with convolutions, In Proc. Of IEEE Conf Comput Vis Pattern Recognit, pp.1–12

  28. Tarel J-P, Hautiere N, Caraffa L, Cord A, Halmaoui H, Gruyer D (2012) Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell Transp Syst Mag 4(2):6–20

    Article  Google Scholar 

  29. Toshev A and Szegedy C Deeppose: human pose estimation via deep neural networks, In Proc. Of IEEE Conf Comput Vis Pattern Recognit, pp.1653–1660, 2014

  30. Wang Y, and Hebert M 2016 Learning to learn: model regression networks for easy small sample learning, In Comput Vis –ECCV pp.1–10

  31. Wang N, and Yeung DY 2013 Learning a deep compact image representation for visual tracking, In Adv Neural Inf Proces Syst, pp.809–817, .

  32. Maji S, Berg A, and Malik J (2008) Classification using intersection kernel support vector machines is efficient, In Proc Of IEEE Comput Vis Pattern Recognit, pp.1–8

  33. Yu K, Zhang T, and Gong Y, (2009) Nonlinear learning using local coordinate coding, In NIPS

  34. Zhang Y (2016) Grorec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput. doi:10.1109/TSC.2016.2592520

    Google Scholar 

  35. Wang S, Zhang Y et al (2016) Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning. Prog Electromagn Res 165:105–133

    Article  Google Scholar 

  36. Zhang Y et al (2016) iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Futur Gener Comput Syst. doi:10.1016/j.future.2015.12.001

    Google Scholar 

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Acknowledgements

This work was supported by JSPS KAKENHI (15F15077, JSPS KAKENHI Grant Number 15 K12562, 15F15077, 16H05913), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Open Research Fund of the Key Laboratory of Marine Geology and Environment in Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (1608), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1301; 1510).

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Authors and Affiliations

  1. Kyushu Institute of Technology, Fukuoka, Japan

    Huimin Lu, Yujie Li, Tomoki Uemura, Seiichi Serikawa & Hyoungseop Kim

  2. Chinese Academy of Science, Beijing, China

    Huimin Lu

  3. Yangzhou University, Yangzhou, China

    Yujie Li

  4. IBM Australia Inc., West Pennant Hills, Australia

    Zongyuan Ge

  5. University of Electronic Science and Technology of China, Chengdu, China

    Xing Xu

  6. Qualcomm Inc., San Diego, USA

    Li He

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  1. Huimin Lu
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  2. Yujie Li
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  3. Tomoki Uemura
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  4. Zongyuan Ge
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  5. Xing Xu
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  6. Li He
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  7. Seiichi Serikawa
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  8. Hyoungseop Kim
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Correspondence to Huimin Lu.

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Lu, H., Li, Y., Uemura, T. et al. FDCNet: filtering deep convolutional network for marine organism classification. Multimed Tools Appl 77, 21847–21860 (2018). https://doi.org/10.1007/s11042-017-4585-1

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  • DOI: https://doi.org/10.1007/s11042-017-4585-1

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