Multimedia Tools and Applications

, Volume 77, Issue 17, pp 21847–21860 | Cite as

FDCNet: filtering deep convolutional network for marine organism classification

  • Huimin LuEmail author
  • Yujie Li
  • Tomoki Uemura
  • Zongyuan Ge
  • Xing Xu
  • Li He
  • Seiichi Serikawa
  • Hyoungseop Kim


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).


Filtering deep convolutional network Marine organism classification Artificial intelligence Deep learning 



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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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Kyushu Institute of TechnologyFukuokaJapan
  2. 2.Chinese Academy of ScienceBeijingChina
  3. 3.Yangzhou UniversityYangzhouChina
  4. 4.IBM Australia Inc.West Pennant HillsAustralia
  5. 5.University of Electronic Science and Technology of ChinaChengduChina
  6. 6.Qualcomm Inc.San DiegoUSA

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