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A Hybrid Convolutional Neural Network for Plankton Classification

  • Jialun Dai
  • Zhibin Yu
  • Haiyong Zheng
  • Bing Zheng
  • Nan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10118)

Abstract

Plankton are fundamental and essential to marine ecosystem, and its survey is significant for sustainable development and ecosystem balance of oceans. The large amount of plankton species and complex relationship among different classes bring difficulty for us to design an automatic plankton classification system. Thus, we develop our model based on convolutional neural network and aim to overcome these shortages. We consider two different ways to extract global and local features to describe shape and texture information of plankton. Furthermore, we design a pyramid fully connected structure to merge different inner products from each sub networks. The experimental results prove our model can take advantage of multiple features and performs better than original convolutional neural network.

Keywords

Support Vector Machine Original Image Convolutional Neural Network Canny Edge Detector Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61271406, 61301240, and the Fundamental Research Funds for the Central Universities under Grant No. 201562023.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jialun Dai
    • 1
  • Zhibin Yu
    • 1
  • Haiyong Zheng
    • 1
  • Bing Zheng
    • 1
  • Nan Wang
    • 1
  1. 1.College of Information Science and EngineeringOcean University of ChinaShandongChina

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