Grimm J, Hoffmann M, Stöver BC, Müller KF, Steinhage V
Research article (book contribution)
Collection and maintenance of biodiversity data is in need for automation. We present first results of an automated and model-free approach to the species identification from herbarium specimens kept in herbaria worldwide. Methodologically, our approach relies on standard methods for the detection and description of so-called interest points and their classification into species-characteristic categories using standard supervised learning tools. To keep the approach model-free on the one hand but also offer opportunities for species identification even in very challenging cases on the other hand, we allow to induce specific knowledge about important visual cues by using concepts of active learning on demand. First encouraging results on selected fern species show recognition accuracies between 94 % and 100 %.
Publisher: Friedrich G, Helmert M, Wotawa F
Book title: KI 2016: Advances in Artificial Intelligence
Pages: 8
Release year: 2016
Publishing company: Springer International Publishing
Language in which the publication is written: English