Human experts vs. machines in taxa recognition
Ärje, J., Raitoharju, J., Iosifidis, A., Tirronen, V., Meissner, K., Gabbouj, M., Kiranyaz, S., & Kärkkäinen, S. (2020). Human experts vs. machines in taxa recognition. Signal Processing : Image Communication, 87, Article 115917. https://doi.org/10.1016/j.image.2020.115917
Published inSignal Processing : Image Communication
© 2020 Elsevier B.V. All rights reserved.
The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. We compare the results of Convolutional Neural Networks to human experts and support vector machines. Our results revealed that human experts using actual specimens yield the lowest classification error (CE¯=6.1%). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy (CE¯=11.4%) when a typical flat classification approach is used. Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts (CE¯=13.8%). Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field. ...
ISSN Search the Publication Forum0923-5965
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Research costs of Academy Research Fellow, AoF
Additional information about fundingWe thank the Academy of Finland for the grants of Ärje (284513, 289076), Tirronen (289076, 289104) Kärkkäinen (289076), Meissner (289104), and Raitoharju (288584). We would like to thank CSC for computational resources.
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