  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.
