Abstract
Studying the variations of the submarine environment at the plankton level can significantly contribute to the preservation of the environment. In situ plankton imaging systems have known an important evolution giving large scale plankton data for organism classification and analysis. Automated classifiers based on Convolutional Neural network are identified as highly efficient methods for image classification but require careful configuration especially for 3D images. In this paper, we propose a CNN architecture for 3D image classification to classify 155 classes of plankton from TARA Oceans dataset in four levels of hierarchical classes. We experiment and compare our proposal denoted C3D2 with competitive CNNs already performed on the case of plankton recognition such as DenseNet and SparseConvNet. Furthermore, we design several methods to incorporate context metadata on CNN architectures in order to boost the performance of the classification model. Finally, we show that C3D2 is more precise than other models. We also show the impact of incorporating context metadata into CNN architecture on different levels of classes.
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Benammar, N., Kahil, H., Titah, A., Calcagno, F.M., Abidi, A., Mabrouk, M.B. (2022). Improving 3D Plankton Image Classification with C3D2 Architecture and Context Metadata. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_17
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