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Joint Feature Learning of Image Data with Embedded Metadata to Leverage Snake Species Classification

Schnelle Fakten

  • Weitere Publizierende

    Mohammadreza Bagherifar

  • Veröffentlichung

    • 2023
  • Sammelband

    Joint Feature Learning of Image Data with Embedded Metadata to Leverage Snake Species Classification (CLEF 2023 Working Notes)

  • Organisationseinheit

  • Fachgebiete

    • Angewandte Informatik
    • Künstliche Intelligenz
  • Forschungsschwerpunkte

    • Medizinische Informatik (MI)
  • Format

    Konferenzpaper

Zitat

B. Bracke, M. Bagherifar, L. Bloch, and C. M. Friedrich, “Joint Feature Learning of Image Data with Embedded Metadata to Leverage Snake Species Classification,” in CLEF 2023 Working Notes, 2023, pp. 2007–2034 [Online]. Available: https://ceur-ws.org/Vol-3497/paper-170.pdf

Abstract

Automatic identification of snake species from non-standard photos is an important task to improve medical treatment of snakebites. To address this problem, the SnakeCLEF 2023 competition provides a large data set of photos and metadata information for 1,784 snake species. This paper describes the FHDO Biomedical Computer Science Group’s (BCSG) participation in this competition. Through a series of experiments investigating the effects of pre-trained feature extractors, image sizes, metadata integrations, class balance learning and multiple instance pooling methods, a proposed model architecture for joint feature learning of image data and embedded metadata is presented to improve classification of snake species. With this proposal, the best model achieved a macro F1-Score of 81.90 % and challenge-specific metrics of 90.09 % Track 1 and 1, 149 Track 2 on the challenge public test data set.

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