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A Novel Feature Extraction Model to Enhance Underwater Image Classification

  • Muhammad IrfanEmail author
  • Jiangbin Zheng
  • Muhammad Iqbal
  • Muhammad Hassan Arif
Conference paper
  • 36 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1187)

Abstract

Underwater images often suffer from scattering and color distortion because of underwater light transportation characteristics and water impurities. Presence of such factors make underwater image classification task very challenging. We propose a novel classification convolution autoencoder (CCAE), which can classify large size underwater images with promising accuracy. CCAE is designed as a hybrid network, which combines benefits of unsupervised convolution autoencoder to extract non-trivial features and a classifier, for better classification accuracy. In order to evaluate classification accuracy of proposed network, experiments are conducted on Fish4Knowledge dataset and underwater synsets of benchmark ImageNet dataset. Classification accuracy, precision, recall and f1-score results are compared with state-of-the-art deep convolutional neural network (CNN) methods. Results show that proposed system can accurately classify large-size underwater images with promising accuracy and outperforms state-of-the-art deep CNN methods. With the proposed network, we expect to advance underwater image classification research and its applications in many areas like ocean biology, sea exploration and aquatic robotics.

Keywords

Convolutional autoencoder Deep learning Convolutional neural network Underwater images 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Muhammad Irfan
    • 1
    Email author
  • Jiangbin Zheng
    • 1
  • Muhammad Iqbal
    • 2
  • Muhammad Hassan Arif
    • 3
  1. 1.School of SoftwareNorthwestern Polytechnical UniversityXianChina
  2. 2.Faculty of Computer and Information ScienceHigher Colleges of TechnologyFujairahUAE
  3. 3.CESATIslamabadPakistan

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