Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Coral classification using DenseNet and Cross-modality transfer learning

Xu, L., Bennamoun, M., Boussaid, F., An, S. and Sohel, F. (2019) Coral classification using DenseNet and Cross-modality transfer learning. In: International Joint Conference on Neural Networks (IJCNN) 2019, 14 - 19 July 2019, Budapest, Hungary

Link to Published Version: https://doi.org/10.1109/IJCNN.2019.8852235
*Subscription may be required

Abstract

Coral classification is a challenging task due to the complex morphology and ambiguous boundaries of corals. This paper investigates the benefits of Densely connected convolutional network (DenseNet) and multi-modal image translation techniques in boosting image classification performance by synthesizing missing fluorescence information. To this end, an imageconditional Generative Adversarial Network (GAN) based image translator is trained to model the relationship between reflectance and fluorescence images. Through this image translator, fluorescence images can be generated from the available reflectance images to provide complementary information. During the classification phase, reflectance and translated fluorescence images are combined to obtain more discriminative representations and produce improved classification performance. We present results on the EFC and MLC datasets and report state-of-the-art coral classification performance.

Item Type: Conference Paper
Murdoch Affiliation: School of Engineering and Information Technology
URI: http://researchrepository.murdoch.edu.au/id/eprint/52106
Item Control Page Item Control Page