Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. (15th March 2019)

• Record Type: Journal Article
• Authors: Gómez-Ríos, Anabel; Tabik, Siham; Luengo, Julián; Shihavuddin, ASM; Krawczyk, Bartosz; Herrera, Francisco
• Is Part Of: Expert systems with applications. Volume 118(2019)
• Page Start: 315
• Page End: 328
• Publication Date: 2019-03-15
• Subjects: Coral images classification -- Deep learning -- Convolutional neural networks -- Inception -- ResNet -- DenseNet; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.10.010
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Study the performance of promising CNNs in the classification of coral texture images. Analyze different types of transfer learning. Analyze data augmentation on the performance of the coral classification model. Experimental results outperform state-of-the-art methods needing human intervention. Generalize the best approach to other coral texture datasets. Graphical abstract: Abstract: The recognition of coral species based on underwater texture images poses 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 reasons, 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 focused on the current small datasets and analyzed (1) several Convolutional Neural Network (CNN) architectures, (2) data augmentation techniques and (3) transfer learning approaches. We have achieved the state-of-the art accuracies using different variations of ResNet on the two small coral texture datasets, EILAT and RSMAS.