Abstract
Ecological studies of some of the most numerous organisms on the planet, zooplankton, have been limited by manual analysis for more than 100 years. With the development of high-throughput video systems, we argue that this critical bottle-neck can now be solved if paired with deep neural networks (DNN). To leverage their performance, large amounts of training samples are required that until now have been dependent on manually created labels. To minimize the effort of expensive human experts, we employ recent active learning approaches to select only the most informative samples for labelling. Thus training a CNN using a nearly unlimited amount of images while limiting the human labelling effort becomes possible by means of active learning. We show in several experiments that in practice, only a few thousand labels are required to train a CNN and achieve an accuracy-level comparable to manual routine analysis of zooplankton samples. Once trained, this CNN can be used to analyse any amount of image data, presenting the zooplankton community the opportunity to address key research questions on transformative scales, many orders of magnitude, in both time and space, basically only limited by video through-put and compute capacity.
T.J.W. Walles was supported by a Leibniz Competition grant ILES “Illuminating Lake Ecosystems”, and technical investment was supported by IGB-startup funds to J.C. Nejstgaard.
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Bochinski, E., Bacha, G., Eiselein, V., Walles, T.J.W., Nejstgaard, J.C., Sikora, T. (2019). Deep Active Learning for In Situ Plankton Classification. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_1
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