A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Ärje, Johanna; Raitoharju, Jenni; Iosifidis, Alexandros; Tirronen, Ville; Meissner, Kristian; Gabbouj, Moncef; Kiranyaz, Serkan; Kärkkäinen, Salme

Lehti tai sarja: Signal Processing : Image Communication

ISSN: 0923-5965

eISSN: 1879-2677

Julkaisuvuosi: 2020

Volyymi: 87

Artikkelinumero: 115917

Kustantaja: Elsevier

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/70919

Julkaisu on rinnakkaistallennettu: https://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.

YSO-asiasanat: systematiikka (biologia); taksonit; hahmontunnistus (tietotekniikka); koneoppiminen; neuroverkot

Vapaat asiasanat: hierarchical classification; taxonomy; convolutional neural networks; taxonomic expert; multi-image data; biomonitoring

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1

Viimeisin päivitys 2022-20-09 klo 13:46