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- Title
FDCNet: filtering deep convolutional network for marine organism classification.
- Authors
Lu, Huimin; Li, Yujie; Uemura, Tomoki; Ge, Zongyuan; Xu, Xing; He, Li; Serikawa, Seiichi; Kim, Hyoungseop
- 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).
- Subjects
SIGNAL convolution; BIOLOGICAL classification; MARINE organisms; IMAGE processing; DEEP learning
- Publication
Multimedia Tools & Applications, 2018, Vol 77, Issue 17, p21847
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-017-4585-1