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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

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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(s): School of Engineering and Information Technology
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