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


Ärje, Johanna; Raitoharju, Jenni; Iosifidis, Alexandros; Tirronen, Ville; Meissner, Kristian; Gabbouj, Moncef; Kiranyaz, Serkan; Kärkkäinen, Salme (2020). Human experts vs. machines in taxa recognition. Signal Processing : Image Communication, 87, 115917. DOI: 10.1016/j.image.2020.115917


JYU authors or editors


Publication details

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

Journal or series: Signal Processing : Image Communication

ISSN: 0923-5965

eISSN: 1879-2677

Publication year: 2020

Volume: 87

Article number: 115917

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.image.2020.115917

Open Access: Publication channel is not openly available

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

Publication is parallel published: https://arxiv.org/abs/1708.06899


Abstract

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.


Keywords: systematics; taxons; pattern recognition; machine learning; neural networks (information technology)

Free keywords: hierarchical classification; taxonomy; convolutional neural networks; taxonomic expert; multi-image data; biomonitoring


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Ministry reporting: Yes

Reporting Year: 2020


Last updated on 2020-18-08 at 13:16