Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation

Anabel Gómez-Ríos*, Siham Tabik, Julían Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.
Original languageEnglish
JournalExpert Systems with Applications
Volume118
Pages (from-to)315–328
ISSN0957-4174
DOIs
Publication statusPublished - 2018

Keywords

  • Coral Images classification
  • Deep Learning
  • Convolutional Neural Networks
  • Inception
  • ResNet
  • DenseNet

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