A1 Journal article (refereed)
Human experts vs. machines in taxa recognition (2020)

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

JYU authors or editors

Publication details

All authors or editorsÄrje, Johanna; Raitoharju, Jenni; Iosifidis, Alexandros; Tirronen, Ville; Meissner, Kristian; Gabbouj, Moncef; Kiranyaz, Serkan; Kärkkäinen, Salme

Journal or seriesSignal Processing : Image Communication



Publication year2020


Article number115917


Publication countryNetherlands

Publication languageEnglish


Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/70919

Publication is parallel publishedhttps://arxiv.org/abs/1708.06899


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.

Keywordssystematicstaxonsautomated pattern recognitionmachine learningneural networks (information technology)

Free keywordshierarchical classification; taxonomy; convolutional neural networks; taxonomic expert; multi-image data; biomonitoring

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Ministry reportingYes

Reporting Year2020

JUFO rating1

Last updated on 2024-22-04 at 11:44